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Mancini, Giacomo, Nicole Righi, Elena Trombini, and Roberta Biolcati. "Intelligenza emotiva di tratto e burnout professionale negli insegnanti di scuola primaria. Una revisione della letteratura." RICERCHE DI PSICOLOGIA, no. 1 (May 2022): 1–22. http://dx.doi.org/10.3280/rip2022oa13705.
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La presente rassegna si propone di esaminare le pubblicazioni scientifiche internazionali che hanno indagato il rapporto tra l'Intelligenza Emotiva (intesa secondo il modello dei tratti e valutata attraverso questionari self-report), e il burnout professionale (caratterizzato da esaurimento emotivo, sentimenti di depersonalizzazione e ridotta autoefficacia) negli insegnanti di scuola primaria. Le recenti ricerche in questo campo, che non sono ancora state sufficientemente sistematizzate, sottolineano infatti l'importanza delle competenze emotive per facilitare e migliorare sia la prestazione lavorativa dei docenti, sia i processi di insegnamento-apprendimento. Alti livelli di Intelligenza Emotiva negli insegnanti sono correlati da un lato a una riduzione dello stress e dell'affaticamento emotivo, e dall'altro ad una maggiore soddisfazione personale nello svolgimento del proprio lavoro; inoltre, sono associati a migliori rapporti con tutti i protagonisti dell'ambiente educativo, con conseguenti effetti positivi sulla qualità della relazione con gli alunni e delle acquisizioni dei saperi.
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Haidar, Abdullah, and Putri Oktavia Rusadi. "A Sentiment Analysis: History of Islamic Economic Thought." Journal of Islamic Economics (JoIE) 2, no. 2 (October 31, 2022): 150–63. http://dx.doi.org/10.21154/joie.v2i2.5082.
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This study reviews the history of Islamic economic thought research in Islamic economics and finance. It uses descriptive statistical analysis based on selected 125 article publications. The entire sample publications have been published from 1984 to 2022. This study analyzes the number of publications based on journal and year, the top authors, the top-cited paper, and the sentiment analysis. The results show that the research of the history of Islamic economic thought throughout the world has a high-positive sentiment of 1%, a positive sentiment of 27%, a negative sentiment of 33%, a high-negative sentiment of 1%, and the rest have a neutral sentiment of 38%. Also, the number of sentiments for these studies has increased in the world community; the most significant number of high-positive sentiments occurred in 2021, with one publication sentiment. Then the most significant number of positive sentiments occurred in 2019, with as many as seven published article sentiments. The most significant number of neutral sentiments occurred in 2018, the same as positive sentiments, seven published article sentiments, and the most significant number of negative sentiments occurred in 2020, six published article sentiments.Penelitian ini mencoba mengkaji sejarah penelitian pemikiran ekonomi Islam di bidang ekonomi dan keuangan Islam. Ini menggunakan analisis statistik deskriptif berdasarkan 125 publikasi artikel yang dipilih. Seluruh sampel publikasi telah diterbitkan dari tahun 1984 hingga 2022. Studi ini menganalisis jumlah publikasi berdasarkan jurnal dan tahun, penulis teratas, makalah yang dikutip teratas, dan analisis sentimen. Hasil penelitian menunjukkan bahwa penelitian sejarah pemikiran ekonomi Islam di seluruh dunia memiliki sentimen positif tinggi 1%, sentimen positif 27%, sentimen negatif 33%, sentimen negatif tinggi 1%, dan selebihnya. memiliki sentimen netral sebesar 38%. Selain itu, jumlah sentimen untuk studi ini telah meningkat di masyarakat dunia, jumlah sentimen positif tinggi terbesar terjadi pada tahun 2021 dengan satu sentimen artikel publikasi. Kemudian jumlah sentimen positif terbesar terjadi pada tahun 2019, yaitu sebanyak tujuh artikel sentimen yang dipublikasikan. Jumlah sentimen netral terbesar terjadi pada tahun 2018, sama dengan sentimen positif yaitu sebanyak tujuh sentimen artikel yang dipublikasikan, dan jumlah sentimen negatif terbesar terjadi pada tahun 2020 yaitu sebanyak enam sentimen artikel yang dipublikasikan.
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Rossi, Roberta, Elisabetta Todaro, Giovanna Torre, and Chiara Simonelli. "Omosessualitŕ e desiderio di genitorialitŕ: indagine esplorativa su un gruppo di omosessuali italiani." RIVISTA DI SESSUOLOGIA CLINICA, no. 1 (July 2010): 23–40. http://dx.doi.org/10.3280/rsc2010-001002.
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Il desiderio di avere un figlio rappresenta un tipo di progettualitŕ multidimensionale, complessiva ed allargata per l'identitŕ individuale e di coppia. L'obiettivo della presente ricerca consiste nell'esplorare la presenza del desiderio di genitorialitŕ in un gruppo di omosessuali italiani, approfondendo le motivazioni ed il grado di riflessivitŕ e d'intensitŕ del desiderio di avere un figlio. La ricerca ha coinvolto 226 soggetti (143 M; 83 F) di etŕ compresa tra i 17 ed i 67 anni (media 31 anni; DS 9.36). Le aree indagate nel presente lavoro sono: dati sociodemografici, l'orientamento sessuale, le motivazioni alla genitorialitŕ (categorie: Benessere; Controllo Sociale; Felicitŕ; Identitŕ; Genitorialitŕ; Continuitŕ), il tempo impiegato a riflettere sui motivi per avere un figlio (Riflessivitŕ) e l'intensitŕ del desiderio alla genitorialitŕ (Intensitŕ del Desiderio). I risultati evidenziano che un'ampia maggioranza del gruppo esprime un desiderio di genitorialitŕ e l'intenzione di portarlo a compimento, con una maggiore rappresentanza delle donne e dei soggetti in coppia. I motivi per desiderare un figlio sono soprattutto legati alla sperimentazione dei sentimenti positivi che la relazione con un figlio comporta e al senso di realizzazione personale e di coppia. Non sono risultate per nulla influenti motivazioni di pressione sociale. L'indagine suggerisce l'importanza di considerare le nuove forme di progettualitŕ espresse dagli omosessuali alla luce di vecchi stereotipi evidenziando la crescita di nuove assertivitŕ nell'affermazione identitaria omosessuale.
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Apif Supriadi and Fatmasari. "Implementasi Metode Klasifikasi Naive Bayes Pada Sistem Analisis Opini Pengguna Twitter Berbasis Web." Jurnal Sistem Informasi 10, no. 1 (February 3, 2021): 46–54. http://dx.doi.org/10.51998/jsi.v10i1.356.
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Abstract— Development of social media which is the result of technological development is an inseparable part of people's lives. Social media is a place where ordinary people express their feelings and opinions about something that concerns them. Inknowing the direction of public sentiment, surveys are usually done online or offline, this sentiment analysis system will facilitate and speed up the process of knowing the direction of public sentiment, in the case of research. This uses data from Twitter social media called tweets or tweets, web-based sentiment analysis system that will classify tweets into 3 (three) types of sentiments, namely positive, neutral and negative, then make a percentage to make it easier to see the direction of public sentiment. In classifying this system uses the Naive Bayes Classifier method and displays it in a web interface with the PHP programming language and uses the Application Programming Interface (API) to get data from Twitter. Intisari — Saat ini perkembangan media sosial yang merupakan hasil dari perkembangan teknologi menjadi bagian tak terpisahkan dari kehidupan masyarakat. Media sosial menjadi tempat masyarakat biasa mengutarakan berbagai perasaan dan opininya tentang suatu hal yang jadi perhatian mereka, dalam mengetahui arah sentimen masyarakat biasanya dilakukan survei baik secara online atau offline, sistem analisis sentimen ini akan memudahkan dan mempercepat proses mengetahui arah sentimen publik, dalam kasus penelitian ini menggunakan data dari media sosial Twitter yang disebut dengan tweets atau cuitan, sistem analisis sentimen berbasis web yang akan mengklasifikasikan cuitan kedalam 3 (tiga) jenis sentimen yaitu positif, netral dan negatif lalu melakukan persentasenya agar mempermudah melihat arah sentimen publik. Dalam melakukan klasifikasinya sistem ini menggunakan metode Naive Bayes Classifier dan menampilkannya dalam antarmuka web dengan bahasa pemrograman PHP dan menggunakan Application Programming Interface (API) dalam mendapatkan data dari Twitter.
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Afzaal, Muhammad, Muhammad Usman, and Alvis Fong. "Predictive aspect-based sentiment classification of online tourist reviews." Journal of Information Science 45, no. 3 (July 25, 2018): 341–63. http://dx.doi.org/10.1177/0165551518789872.
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With the increase of online tourists reviews, discovering sentimental idea regarding a tourist place through the posted reviews is becoming a challenging task. The presence of various aspects discussed in user reviews makes it even harder to accurately extract and classify the sentiments. Aspect-based sentiment analysis aims to extract and classify user’s positive or negative orientation towards each aspect. Although several aspect-based sentiment classification methods have been proposed in the past, limited work has been targeted towards the automatic extraction of implicit, infrequent and co-referential aspects. Moreover, existing methods lack the ability to accurately classify the overall polarity of multi-aspect sentiments. This study aims to develop a predictive framework for aspect-based extraction and classification. The proposed framework utilises the semantic relations among review phrases to extract implicit and infrequent aspects for accurate sentiment predictions. Experiments have been performed using real-world data sets crawled from predominant tourist websites such as TripAdvisor and OpenTable. Experimental results and comparison with previously reported findings prove that the predictive framework not only extracts the aspects effectively but also improves the prediction accuracy of aspects.
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Huang, Changqin, Zhongmei Han, Ming Li, Xizhe Wang, and Wenzhu Zhao. "Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis." Australasian Journal of Educational Technology 37, no. 2 (May 10, 2021): 81–95. http://dx.doi.org/10.14742/ajet.6749.
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Sentiment evolution is a key component of interactions in blended learning. Although interactions have attracted considerable attention in online learning contexts, there is scant research on examining sentiment evolution over different interactions in blended learning environments. Thus, in this study, sentiment evolution at different interaction levels was investigated from the longitudinal data of five learning stages of 38 postgraduate students in a blended learning course. Specifically, text mining techniques were employed to mine the sentiments in different interactions, and then epistemic network analysis (ENA) was used to uncover sentiment changes in the five learning stages of blended learning. The findings suggested that negative sentiments were moderately associated with several other sentiments such as joking, confused, and neutral sentiments in blended learning contexts. Particularly in relation to deep interactions, student sentiments might change from negative to insightful ones. In contrast, the sentiment network built from social-emotion interactions shows stronger connections in joking-positive and joking-negative sentiments than the other two interaction levels. Most notably, the changes of co-occurrence sentiment reveal the three periods in a blended learning process, namely initial, collision and sublimation, and stable periods. The results in this study revealed that students’ sentiments evolved from positive to confused/negative to insightful.
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Zhou, Xinyi, Shengmin Jin, and Reza Zafarani. "Sentiment Paradoxes in Social Networks: Why Your Friends Are More Positive Than You?" Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 798–807. http://dx.doi.org/10.1609/icwsm.v14i1.7344.
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Most people consider their friends to be more positive than themselves, exhibiting a Sentiment Paradox. Psychology research attributes this paradox to human cognition bias. With the goal to understand this phenomenon, we study sentiment paradoxes in social networks. Our work shows that social connections (friends, followees, or followers) of users are indeed (not just illusively) more positive than the users themselves. This is mostly due to positive users having more friends. We identify five sentiment paradoxes at different network levels ranging from triads to large-scale communities. Empirical and theoretical evidence are provided to validate the existence of such sentiment paradoxes. By investigating the relationships between the sentiment paradox and other well-developed network paradoxes, i.e., friendship paradox and activity paradox, we find that user sentiments are positively correlated to their number of friends but rarely to their social activity. Finally, we demonstrate how sentiment paradoxes can be used to predict user sentiments.
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Kumar, Abhishek, Vishal Dutt, Vicente García-Díaz, and Sushil Kumar Narang. "Twitter sentimental analysis from time series facts: the implementation of enhanced support vector machine." Bulletin of Electrical Engineering and Informatics 10, no. 5 (October 1, 2021): 2845–56. http://dx.doi.org/10.11591/eei.v10i5.3078.
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Sentiment analysis through textual data mining is an indispensable system used to extract the contextual social information from the texts submitted by the intended users. Now days, world wide web is playing a vital source of textual content being shared in different communities by the people sharing their own sentiments through the websites or web blogs. Sentiment analysis has become a vital field of study since based on the extracted expressions, individuals or the businesses can access or update their reviews and take significant decisions. Sentimental mining is typically used to classify these reviews depending on its assessment as whether these reviews come out to be neutral, positive or negative. In our study, we have boosted feature selection technique with strong feature normalization for classifying the sentiments into negative, positive or neutral. Afterwards, support vector machine (SVM) classifier powered with radial basis kernel with adjusted hyper plane parameters, was employed to categorize reviews. Grid search with cross validation as well as logarithmic scale were employed for optimal values of hyper parameters. The classification results of this proposed system provides optimal results when compared to other state of art classification methods.
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Gao, Xiang, Weige Huang, and Hua Wang. "Financial Twitter Sentiment on Bitcoin Return and High-Frequency Volatility." Virtual Economics 4, no. 1 (January 31, 2021): 7–18. http://dx.doi.org/10.34021/ve.2021.04.01(1).
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This paper studies how sentiment affect Bitcoin pricing by examining, at an hourly frequency, the linkage between sentiment of finance-related Twitter messages and return as well as the volatility of Bitcoin as a financial asset. On the one hand, there was calculated the return from minute-level Bitcoin exchange quotes and use of both rolling variance and high-minus-low price to proxy for Bitcoin volatility per each trading hour. On the other hand, the mood signals from tweets were extracted based on a list of positive, negative, and uncertain words according to the Loughran-McDonald finance-specific dictionary. These signals were translated by categorizing each tweet into one of three sentiments, namely, bullish, bearish, and null. Then the total number of tweets were adopted in each category over one hour and their differences as potential Bitcoin price predictors. The empirical results indicate that after controlling a list of lagged returns and volatilities, stronger bullish sentiment significantly foreshadows higher Bitcoin return and volatility over the time range of 24 hours. While bearish and neutral financial Twitter sentiments have no such consistent performance, the difference between bullish and bearish ratings can improve prediction consistency. Overall, this research results add to the growing Bitcoin literature by demonstrating that the Bitcoin pricing mechanism can be partially revealed by the momentum on sentiment in social media networks, justifying a sentimental appetite for cryptocurrency investment.
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Mushtaq, Muhammad Faheem, Mian Muhammad Sadiq Fareed, Mubarak Almutairi, Saleem Ullah, Gulnaz Ahmed, and Kashif Munir. "Analyses of Public Attention and Sentiments towards Different COVID-19 Vaccines Using Data Mining Techniques." Vaccines 10, no. 5 (April 22, 2022): 661. http://dx.doi.org/10.3390/vaccines10050661.
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COVID-19 is a widely spread disease, and in order to overcome its spread, vaccination is necessary. Different vaccines are available in the market and people have different sentiments about different vaccines. This study aims to identify variations and explore temporal trends in the sentiments of tweets related to different COVID-19 vaccines (Covaxin, Moderna, Pfizer, and Sinopharm). We used the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool to analyze the public sentiments related to each vaccine separately and identify whether the sentiments are positive (compound ≥ 0.05), negative (compound ≤ −0.05), or neutral (−0.05 < compound < 0.05). Then, we analyzed tweets related to each vaccine further to find the time trends and geographical distribution of sentiments in different regions. According to our data, overall sentiments about each vaccine are neutral. Covaxin is associated with 28% positive sentiments and Moderna with 37% positive sentiments. In the temporal analysis, we found that tweets related to each vaccine increased in different time frames. Pfizer- and Sinopharm-related tweets increased in August 2021, whereas tweets related to Covaxin increased in July 2021. Geographically, the highest sentiment score (0.9682) is for Covaxin from India, while Moderna has the highest sentiment score (0.9638) from the USA. Overall, this study shows that public sentiments about COVID-19 vaccines have changed over time and geographically. The sentiment analysis can give insights into time trends that can help policymakers to develop their policies according to the requirements and enhance vaccination programs.
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Madani, Youness, Mohammed Erritali, Jamaa Bengourram, and Francoise Sailhan. "Social Network Analysis." Journal of Information Technology Research 13, no. 3 (July 2020): 142–55. http://dx.doi.org/10.4018/jitr.2020070109.
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Sentiment analysis has become an important field in scientific research in recent years. The goal is to extract opinions and sentiments from written text using artificial intelligence algorithms. In this article, we propose a new approach for classifying Twitter data into classes (positive, negative, and neutral). The proposed method is based on two approaches, a dictionary-based approach using the sentimental dictionary SentiWordNet, and an approach based on the fuzzy logic system (fuzzification, rule inference, and defuzzification). Experimental results show that our approach outperforms some other approaches in the literature and that by using the fuzzy logic we improve the quality of the classification.
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Gai, Zhenyu, Chenjing Fan, Shiguang Shen, Yanling Ge, Zhan Shi, Shiqi Li, Yiyang Zhang, and Yirui Cao. "Using Social Media Data to Explore Urban Land Value and Sentiment Inequality: A Case Study of Xiamen, China." Wireless Communications and Mobile Computing 2022 (September 15, 2022): 1–14. http://dx.doi.org/10.1155/2022/1456382.
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Differences in urban land values affect residents’ living experiences and may contribute to sentiment inequality. Due to the popularity of smart mobile devices and social media platforms, online tweets with location information can be used as objective information to reflect sentiment differences of urban residents in different locations, overcoming the limitations of previous studies with small sample sizes or a lack of spatial information. Sentiment quantification based on deep learning enables the identification of spatial patterns of urban residents’ sentiments. It also provides a new approach for analyzing data from big data platforms using an intelligent computing platform. This paper quantitatively analyzes the sentiment contained in social media tweets using a deep learning sentiment analysis algorithm to reveal inequalities between urban residents’ sentiments and land values. The Baidu Intelligent Cloud sentiment analysis platform is used to identify 460,000 Weibo tweets in Xiamen, China, in 2020. We quantitatively analyze the positive and negative sentiments of residents and create a spatial distribution map. The concentration curve indicates sentiment inequality and the impact of high land values on residents’ sentiments. The positive sentiment concentration index (CI) and correlation analysis show that the CI value is 0.07, and significant sentiment inequality exists due to the high land value. The use of social media tweet data to analyze sentiment inequality provides a reference for future interdisciplinary research in psychology, urban planning, geography, and sociology. The proposed approach of analyzing social media data using an intelligent computing platform provides new insights into multiplatform data interaction in the context of the Internet of Everything.
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Aggarwal, Divya, and Pitabas Mohanty. "Do Indian stock market sentiments impact contemporaneous returns?" South Asian Journal of Business Studies 7, no. 3 (October 1, 2018): 332–46. http://dx.doi.org/10.1108/sajbs-06-2018-0064.
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Purpose The purpose of this paper is to analyse the impact of Indian investor sentiments on contemporaneous stock returns of Bombay Stock Exchange, National Stock Exchange and various sectoral indices in India by developing a sentiment index. Design/methodology/approach The study uses principal component analysis to develop a sentiment index as a proxy for Indian stock market sentiments over a time frame from April 1996 to January 2017. It uses an exploratory approach to identify relevant proxies in building a sentiment index using indirect market measures and macro variables of Indian and US markets. Findings The study finds that there is a significant positive correlation between the sentiment index and stock index returns. Sectors which are more dependent on institutional fund flows show a significant impact of the change in sentiments on their respective sectoral indices. Research limitations/implications The study has used data at a monthly frequency. Analysing higher frequency data can explain short-term temporal dynamics between sentiments and returns better. Further studies can be done to explore whether sentiments can be used to predict stock returns. Practical implications The results imply that one can develop profitable trading strategies by investing in sectors like metals and capital goods, which are more susceptible to generate positive returns when the sentiment index is high. Originality/value The study supplements the existing literature on the impact of investor sentiments on contemporaneous stock returns in the context of a developing market. It identifies relevant proxies of investor sentiments for the Indian stock market.
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Wiguna, Bagus Satria, Cinthia Vairra Hudiyanti, Alqis Alqis Rausanfita, and Agus Zainal Arifin. "Sarcasm Detection Engine for Twitter Sentiment Analysis using Textual and Emoji Feature." Jurnal Ilmu Komputer dan Informasi 14, no. 1 (February 28, 2021): 1–8. http://dx.doi.org/10.21609/jiki.v14i1.812.
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Twitter is a social media platform that is used to express sentiments about events, topics, individuals, and groups. Sentiments in Tweets can be classified as positive or negative expressions. However, in sentiment, there is an expression that is actually the opposite of what is mean to be, and this is called sarcasm. The existence of sarcasm in a Tweet is difficult to detect automatically by a system even by humans. In this research, we propose a weighting scheme based on inconsistency between sentimen of tweet contain in Indonesian and the usage of emoji. With the weighting scheme for the detection of sarcasm, it can be used to find out a sentiment about a event, topic, individual, group, or product's review. The proposed method is by calculating the distance between the textual feature polarity score obtained from the Convolutional Neural Network and the emoji polarity score in a Tweet. This method is used to find the boundary value between Tweets that contain sarcasm or not. The experimental results of the model developed, obtained f1-score 87.5%, precision 90.5% and recall 84.8%. By using the textual features and emoji models, it can detect sarcasm in a Tweet.
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Huang, Robin, Na Liu, Mary Ann Nicdao, Mary Mikaheal, Tanya Baldacchino, Annabelle Albeos, Kathy Petoumenos, Kamal Sud, and Jinman Kim. "Emotion sharing in remote patient monitoring of patients with chronic kidney disease." Journal of the American Medical Informatics Association 27, no. 2 (October 21, 2019): 185–93. http://dx.doi.org/10.1093/jamia/ocz183.
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Abstract Objective To investigate the relationship between emotion sharing and technically troubled dialysis (TTD) in a remote patient monitoring (RPM) setting. Materials and Methods A custom software system was developed for home hemodialysis patients to use in an RPM setting, with focus on emoticon sharing and sentiment analysis of patients’ text data. We analyzed the outcome of emoticon and sentiment against TTD. Logistic regression was used to assess the relationship between patients’ emotions (emoticon and sentiment) and TTD. Results Usage data were collected from January 1, 2015 to June 1, 2018 from 156 patients that actively used the app system, with a total of 31 159 dialysis sessions recorded. Overall, 122 patients (78%) made use of the emoticon feature while 146 patients (94%) wrote at least 1 or more session notes for sentiment analysis. In total, 4087 (13%) sessions were classified as TTD. In the multivariate model, when compared to sessions with self-reported very happy emoticons, those with sad emoticons showed significantly higher associations to TTD (aOR 4.97; 95% CI 4.13–5.99; P = < .001). Similarly, negative sentiments also revealed significant associations to TTD (aOR 1.56; 95% CI 1.22–2; P = .003) when compared to positive sentiments. Discussion The distribution of emoticons varied greatly when compared to sentiment analysis outcomes due to the differences in the design features. The emoticon feature was generally easier to understand and quicker to input while the sentiment analysis required patients to manually input their personal thoughts. Conclusion Patients on home hemodialysis actively expressed their emotions during RPM. Negative emotions were found to have significant associations with TTD. The use of emoticons and sentimental analysis may be used as a predictive indicator for prolonged TTD.
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Sayim, Mustafa, and Hamid Rahman. "The relationship between individual investor sentiment, stock return and volatility." International Journal of Emerging Markets 10, no. 3 (July 20, 2015): 504–20. http://dx.doi.org/10.1108/ijoem-07-2012-0060.
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Purpose – The purpose of this paper is to examine the impact of Turkish individual investor sentiment on the Istanbul Stock Exchange (ISE) and to investigate whether investor sentiment, stock return and volatility in Turkey are related. Design/methodology/approach – This study used the monthly Turkish Consumer Confidence Index, published by the Turkish Statistical Institute, as a proxy for individual investor sentiments. First, Turkish market fundamentals were regressed on investor sentiments in order to capture the effects of macroeconomic risk factors on investor sentiments. Then, it used the impulse response functions (IRFs) generated from the vector autoregression (VAR) model to examine the effect of unanticipated movements in Turkish investor sentiment to both stock returns and volatility of the ISE. Findings – The generalized IRFs from VAR shows that unexpected changes in rational and irrational investor sentiment have a significant positive impact on ISE returns. This suggests that a positive investor sentiment tends to increase ISE returns. The study also documents that unanticipated increase in the rational component of Turkish investor sentiment has a negative significant effect on ISE volatility. This might indicate that investors have optimistic expectations of the economy overall with respect to market fundamentals in Turkey. This optimism can result in creating positive expectations, reducing uncertainty, and reducing the volatility of stock market returns. Research limitations/implications – The study was applied only for the period 2004-2010 on the ISE stock returns and volatility. Practical implications – Regardless, investors should know the impact of irrational investor sentiments while establishing investment strategies. The results of this study may also help policy makers stabilize investor sentiments to reduce stock market volatility and uncertainty. Originality/value – This paper adds to the limited understanding of investor sentiment impact on stock return and volatility in an emerging market context.
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Manurip, Kevin, and Debi Irawan. "Analisis Sentimen Distribusi Vaksin COVID-19 di Indonesia Menggunakan Algoritma Naïve Bayes Classifier." Jurnal Ilmiah Universitas Batanghari Jambi 22, no. 2 (July 26, 2022): 1205. http://dx.doi.org/10.33087/jiubj.v22i2.2397.
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Since the Indonesian government officially announced the first case of COVID-19, traditional media and social media content related to COVID-19 has increased dramatically. On the one hand, the media talks about prevention, symptom recognition and about prevention, symptom recognition and treatment are massive. Sentiment Analysis or commonly called opinion mining, is a field of study that analyzes opinions, sentiments, evaluations, judgments, attitudes, and emotions towards entities and is implemented on social media content. This becomes interesting and important for certain parties who want to know the good and bad sentiments or opinions given by the Indonesian people towards the distribution of vaccines for the handling of COVID-19. From this research, the level of capability of the system that has been built to find the accuracy between the information requested by the user on the Sinovac vaccine results from a total of 1524 tweets, there are 819 positive tweets, 452 neutral tweets, and 253 negative tweets. The results of the AstraZeneca vaccine classification resulted in 211 tweets with a total of 100 positive sentiments, 80 tweets of neutral sentiment, and 31 tweets of negative sentiment. Sentiment classification results based on scraping data with the keyword Astrazeneca vaccine, resulted in 1266 tweets with a positive sentiment value of 712 tweets, neutral sentiment as many as 344 tweets, and negative sentiment as many as 210 tweets.
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Shabaz, Mohammad, and Ashok Kumar. "AS: a novel sentimental analysis approach." International Journal of Engineering & Technology 7, no. 2.18 (June 5, 2018): 46. http://dx.doi.org/10.14419/ijet.v7i2.27.11679.
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It is interesting to know about the things which are unknown. Just like the emotions, feelings, opinion, sentiments. The term sentimental analysis relates to all these above discussed terms. The extraction of opinion or sentiments from the data through the analysis is called sentimental analysis. There are different kinds of approaches but deal with extraction of sentiments. But none of the approach gives the accurate results. Since sentiments are directly related to human behaviour and human behaviour is not algorithmic. In this paper we have designed another novel approach named as AS analysis. AS analysis is one of new design to overcome the limitation of positive and negative word comparison with input text after tokenization. In this approach we have designed a new formula to find the sentiments count that ranges from -1 to +1 where negative values denotes the negative sentiments and positive values denotes the positive sentiments and 0 denotes the neutral sentiments. By this approach we conclude that the results we have obtained are near to accurate since there is no measure of accuracy it varies from individual to individual, so performing sentimental analysis always gives approximate results.
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Wang, Chen-Ya, Yi-Chun Lin, Hsia-Ching Chang, and Seng-cho T. Chou. "Consumer Sentiment in Tweets and Coupon Information-Sharing Behavior." International Journal of Online Marketing 7, no. 3 (July 2017): 1–19. http://dx.doi.org/10.4018/ijom.2017070101.
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The authors aim to explore the correlation between coupon information-sharing behavior and consumer sentiment by analyzing tweets. They used Twitter application programming interface to retrieve users' tweets, and took a machine learning approach for sentiment analysis. After the data pre-processing procedure, the authors then examined the correlation between sentiments in tweets and coupon information sharing. More than half of the most active users showed that their coupon information-sharing behavior correlated to both positive and negative sentiments. The results also showed that the response, coupon information sharing, for positive/negative sentiment had no significant time shifting pattern for most of the users. This study preliminary verifies the assumption that there is a correlation between users' sentiments in tweets and coupon information-sharing behavior, and indicates some interesting findings. The authors' findings may shed light on whether sentiment plays a role in social media communication concerning the sharing of coupon information.
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Jang, Hyeju, Emily Rempel, Ian Roe, Prince Adu, Giuseppe Carenini, and Naveed Zafar Janjua. "Tracking Public Attitudes Toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-Based Sentiment Analysis." Journal of Medical Internet Research 24, no. 3 (March 29, 2022): e35016. http://dx.doi.org/10.2196/35016.
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Background The development and approval of COVID-19 vaccines have generated optimism for the end of the COVID-19 pandemic and a return to normalcy. However, vaccine hesitancy, often fueled by misinformation, poses a major barrier to achieving herd immunity. Objective We aim to investigate Twitter users’ attitudes toward COVID-19 vaccination in Canada after vaccine rollout. Methods We applied a weakly supervised aspect-based sentiment analysis (ABSA) technique, which involves the human-in-the-loop system, on COVID-19 vaccination–related tweets in Canada. Automatically generated aspect and opinion terms were manually corrected by public health experts to ensure the accuracy of the terms and make them more domain-specific. Then, based on these manually corrected terms, the system inferred sentiments toward the aspects. We observed sentiments toward key aspects related to COVID-19 vaccination, and investigated how sentiments toward “vaccination” changed over time. In addition, we analyzed the most retweeted or liked tweets by observing most frequent nouns and sentiments toward key aspects. Results After applying the ABSA system, we obtained 170 aspect terms (eg, “immunity” and “pfizer”) and 6775 opinion terms (eg, “trustworthy” for the positive sentiment and “jeopardize” for the negative sentiment). While manually verifying or editing these terms, our public health experts selected 20 key aspects related to COVID-19 vaccination for analysis. The sentiment analysis results for the 20 key aspects revealed negative sentiments related to “vaccine distribution,” “side effects,” “allergy,” “reactions,” and “anti-vaxxer,” and positive sentiments related to “vaccine campaign,” “vaccine candidates,” and “immune response.” These results indicate that the Twitter users express concerns about the safety of vaccines but still consider vaccines as the option to end the pandemic. In addition, compared to the sentiment of the remaining tweets, the most retweeted or liked tweets showed more positive sentiment overall toward key aspects (P<.001), especially vaccines (P<.001) and vaccination (P=.009). Further investigation of the most retweeted or liked tweets revealed two opposing trends in Twitter users who showed negative sentiments toward vaccines: the “anti-vaxxer” population that used negative sentiments as a means to discourage vaccination and the “Covid Zero” population that used negative sentiments to encourage vaccinations while critiquing the public health response. Conclusions Our study examined public sentiments toward COVID-19 vaccination on tweets over an extended period in Canada. Our findings could inform public health agencies to design and implement interventions to promote vaccination.
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Jiang, Cuiqing, Jianfei Wang, Qian Tang, and Xiaozhong Lyu. "Investigating the Effects of Dimension-Specific Sentiments on Product Sales: The Perspective of Sentiment Preferences." Journal of the Association for Information Systems 22, no. 2 (2021): 459–89. http://dx.doi.org/10.17705/1jais.00668.
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While the literature has reached a consensus on the awareness effect of online word-of-mouth (eWOM), this paper studies its persuasive effect—specifically, dimension-specific sentiment effects on product sales.We examine the sentiment information in eWOM along different product dimensions and reveal different persuasive effects on consumers’ purchase decisions based on consumers’ sentiment preference, which is defined as the relative importance that consumers place on various dimension-specific sentiments. We use an aspect-level sentiment analysis to derive dimension-specific sentiment and PVAR (panel vector auto-regression) models, and estimate their effects on product sales using a movie panel dataset. The findings show that three dimension-specific sentiments (star, genre, and plot) are positively related to movie sales.Regarding consumers’ sentiment preferences, we find a positive relationship to movie sales that is stronger for plot sentiment, relative to star sentiment for low-budget movies. For high-budget movies, we find a positive relationship to movie sales that is stronger for star sentiment, relative to plot or genre sentiment.
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Kuang, Jiaqi, Xudong Ji, Peng Cheng, and Vasileios Kallinterakis. "Media News and Social Media Information in the Chinese Peer-to-Peer Lending Market." Systems 11, no. 3 (March 1, 2023): 133. http://dx.doi.org/10.3390/systems11030133.
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This paper uses supervised machine learning (sentiment analysis) to analyze the sentiments of social media information in the P2P lending market. After segmentation, filtering, feature word extraction, and model training of the text information captured by Python, the sentiments of media and social media information were calculated to examine the effect of media and social media sentiments on default probability and cost of capital of peer-to-peer (P2P) lending platforms in China (2015–2019). We find that only positive changes in media and social media sentiment have significantly negative effects on the platform’s default probability and cost of capital, while negative changes in sentiment do not have any effects. We conclude the existence of an asymmetric effect of media and social media sentiments in the Chinese peer-to-peer lending market.
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Azkiya, Azka Al, Iliana Patricia Vega, M. Iqbal, Zahra Nurul Fatimah, and Utami Dyah Syafitri. "Kata Netizen tentang Kesetaraan Gender dalam Sentimen Warganet Twitter." Martabat: Jurnal Perempuan dan Anak 5, no. 2 (December 20, 2021): 434–58. http://dx.doi.org/10.21274/martabat.2021.5.2.434-458.
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Abstract: Gender equality is one of the goals in the Sustainable Development Goals. However, until now Indonesia is still having difficulties in achieving this goal. According to the United Nations Development Program (UNDP) data, Indonesia's Gender Inequality Index (GII) is ranked 107 out of 189 countries. In addition, according to The Global Gender Gap Index 2021 data by the World Economic Forum (WEF), Indonesia is ranked 105th out of 153 countries. This shows that Indonesia is still lagging behind in terms of gender equality. Therefore, this study aims to analyze the sentiments of Indonesian twitter netizens regarding gender equality in 2018-2021 and its accuracy. Data was collected from primary data, scraping twitter data with the keywords #kesetaraan and #gender in Indonesian. The method used is Lexicon-based Sentiment Analysis with AFINN-111 dictionary translated into Indonesian. The results obtained are that the percentage of positive sentiments tends to decrease from year to year except for 2021. On the contrary, the negative sentiments of Twitter tend to increase. This is due to controversial articles in RKUHP, RUU Cipta Kerja, Covid-19 pandemic, and the online gender-based violence. This shows that the gender equality in Indonesia is still minimal and needs to be improved.
Keywords: AFINN-111, gender equality, lexicon-based sentiment analysis, text mining, twitter
Abstrak: Kesetaraan gender termasuk tujuan pada Sustainable Development Goals. Namun hingga saat ini Indonesia masih kesulitan dalam mencapai tujuan tersebut. Menurut data United Nations Development Programme (UNDP), nilai Gender Inequality Index (GII) Indonesia menempati peringkat 107 dari 189 negara. Selain itu, menurut data The Global Gender Gap Index 2021 dari World Economic Forum (WEF), Indonesia menempati posisi ke-105 dari total 153 negara. Hal ini membuktikan gender di Indonesia masih belum setara. Oleh karena itu, penelitian ini bertujuan untuk menganalisis sentiment netizen twitter Indonesia mengenai kesetaraan gender pada 2018-202i dan akurasinya. Data dikumpulkan dari data primer yaitu scraping data twitter dengan keyword #kesetaraangender dan #gender dalam Bahasa Indonesia. Metode yang digunakan adalah Lexicon-based Sentiment Analysis dengan bantuan kamus AFINN-111 yang diterjemahkan dalam Bahasa Indonesia pada software python. Hasil yang diperoleh adalah persentase sentimen positif netizen twitter cenderung menurun dari tahun ke tahun kecuali 2021, sebaliknya sentimen negatif netizen twitter cenderung meningkat setiap tahun. Hal ini dikarenakan adanya pasal yang mengandung kontroversi pada Rancangan Kitab Undang-undang Hukum Pidana (RKUHP), RUU Cipta Kerja, adanya pandemi Covid-19, dan maraknya kekerasan berbasis gender online. Hal ini menunjukkan bahwa tingkat kesetaraan gender di Indonesia masih minim dan perlu untuk ditingkatkan kedepannya.
Kata kunci: AFINN-111, kesetaraan gender, lexicon-based sentiment analysis, text mining, twitter
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Hung, Man, Evelyn Lauren, Eric S. Hon, Wendy C. Birmingham, Julie Xu, Sharon Su, Shirley D. Hon, Jungweon Park, Peter Dang, and Martin S. Lipsky. "Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence." Journal of Medical Internet Research 22, no. 8 (August 18, 2020): e22590. http://dx.doi.org/10.2196/22590.
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Background The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. Objective The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19. Methods This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19–related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. Results There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19–related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment. Conclusions This study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public’s response to COVID-19 and help officials navigate the pandemic.
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Silaen, Oriza Sativa Dinauni, Herlawati Herlawati, and Rasim Rasim. "Analisis Sentimen Mengenai Gangguan Bipolar Pada Twitter Menggunakan Algoritma Naïve Bayes." Jurnal Komtika (Komputasi dan Informatika) 6, no. 2 (November 28, 2022): 62–73. http://dx.doi.org/10.31603/komtika.v6i2.8198.
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Bipolar disorder is one of the world's most common mental health disorders. To find out public sentiment regarding bipolar disorder, sentiment analysis is carried out through social media to analyze positive or negative sentiments with the aim of maintaining positive sentiment towards the problem of bipolar disorder. Twitter is a social media that is often used to exchange information, discuss, and even express emotions. The emotions of Twitter users can be called sentiment. Sentiment analysis is also carried out to see opinions or tendencies towards an opinion. Opinion tendencies can be in the form of positive or negative sentiments. The data used in this study uses the bipolar keyword. There are 2177 tweets data that were successfully obtained in the crawling process using API key access from Twitter developers, after which the data will be processed using preprocessing. The comparison of the presentations obtained is 70.92% expressing a negative opinion and 29.08% expressing a favorable opinion. The analysis results in this study using the nave Bayes algorithm is with an accuracy value of 92.110092%.
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Huang, Yin-Fu, and Yi-Hao Li. "Translating Sentimental Statements Using Deep Learning Techniques." Electronics 10, no. 2 (January 10, 2021): 138. http://dx.doi.org/10.3390/electronics10020138.
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Natural Language Processing (NLP) allows machines to know nature languages and helps us do tasks, such as retrieving information, answering questions, text summarization, categorizing text, and machine translation. To our understanding, no NLP was used to translate statements from negative sentiment to positive sentiment with resembling semantics, although human communication needs. The developments of translating sentimental statements using deep learning techniques are proposed in this paper. First, for a sentiment translation model, we create negative–positive sentimental statement datasets. Then using deep learning techniques, the sentiment translation model is developed. Perplexity, bilingual evaluation understudy, and human evaluations are used in the experiments to test the model, and the results are satisfactory. Finally, if the trained datasets can be constructed as planned, we believe the techniques used in translating sentimental statements are possible, and more sophisticated models can be developed.
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Huang, Yin-Fu, and Yi-Hao Li. "Translating Sentimental Statements Using Deep Learning Techniques." Electronics 10, no. 2 (January 10, 2021): 138. http://dx.doi.org/10.3390/electronics10020138.
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Natural Language Processing (NLP) allows machines to know nature languages and helps us do tasks, such as retrieving information, answering questions, text summarization, categorizing text, and machine translation. To our understanding, no NLP was used to translate statements from negative sentiment to positive sentiment with resembling semantics, although human communication needs. The developments of translating sentimental statements using deep learning techniques are proposed in this paper. First, for a sentiment translation model, we create negative–positive sentimental statement datasets. Then using deep learning techniques, the sentiment translation model is developed. Perplexity, bilingual evaluation understudy, and human evaluations are used in the experiments to test the model, and the results are satisfactory. Finally, if the trained datasets can be constructed as planned, we believe the techniques used in translating sentimental statements are possible, and more sophisticated models can be developed.
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Raharjana, Indra Kharisma, Via Aprillya, Badrus Zaman, Army Justitia, and Shukor Sanim Mohd Fauzi. "Enhancing Software Feature Extraction Results Using Sentiment Analysis to Aid Requirements Reuse." Computers 10, no. 3 (March 19, 2021): 36. http://dx.doi.org/10.3390/computers10030036.
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Recently, feature extraction from user reviews has been used for requirements reuse to improve the software development process. However, research has yet to use sentiment analysis in the extraction for it to be well understood. The aim of this study is to improve software feature extraction results by using sentiment analysis. Our study’s novelty focuses on the correlation between feature extraction from user reviews and results of sentiment analysis for requirement reuse. This study can inform system analysis in the requirements elicitation process. Our proposal uses user reviews for the software feature extraction and incorporates sentiment analysis and similarity measures in the process. Experimental results show that the extracted features used to expand existing requirements may come from positive and negative sentiments. However, extracted features with positive sentiment overall have better values than negative sentiments, namely 90% compared to 63% for the relevance value, 74–47% for prompting new features, and 55–26% for verbatim reuse as new requirements.
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Priya, P. Santhi, and T. Venkate swara Rao. "Analysing Event-Related Sentiments on Social Media with Neural Networks." IAES International Journal of Artificial Intelligence (IJ-AI) 7, no. 3 (August 6, 2018): 119. http://dx.doi.org/10.11591/ijai.v7.i3.pp119-124.
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<span lang="EN-US">Sentiment analysis is performed to determine the polarity of opinion on a subject. It has been applied to text corpora such as movie reviews, financial documents to glean information about overall-sentiment anc produce actionable data. Recent events have demonstrated that polling can be sometimes unreliable. People can be difficult to access through conventional polling methods and less than frank in polls. In the era of social media, voters are likely to more freely express their opinion on social media forums about divisive events especially in media where anonymity exists. Analyzing the prevailing opinion on these forums can indicate if there are any deficiencies in polling and can be a valuable addition to conventional polling. We analyzed text corpora from Reddit forums discussing the recent referendum in Britain to exit from the EU (known as Brexit). Brexit was an important world event and was very divisive in the run-up and post vote. We analyzed sentiment in two ways: Initially we tried to gauge positive, negative, and neutral sentiments. In the second analysis, we further split these sentiments into six different polarities based on the directionality of the positive and negative sentiments (for or against Brexit). Our technique utlilized paragraph vectors (Doc2Vec) to construct feature vectors for sentiment analysis with a Multilayer Perceptron classifier. We found that the second analysis yielded overall better results; although, our classifier didn’t perform as well in classifying positive sentiments. We demonstrate that it is possible glean valuable information from complicated and diverse corpora such as multi-paragraph comments from reddit with sentiment analysis.</span>
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Wang, Zhaoxia, Seng-Beng Ho, and Erik Cambria. "Multi-Level Fine-Scaled Sentiment Sensing with Ambivalence Handling." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. 04 (August 2020): 683–97. http://dx.doi.org/10.1142/s0218488520500294.
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Social media represent a rich source of information, such as critiques, feedback, and other opinions posted online by Internet users. Such information is typically a good reflection of users’ sentiments and attitudes towards various services, topics, or products. Sentiment analysis has become an increasingly important natural language processing (NLP) task to help users make sense of what is happening in the Internet blogosphere and it can be useful for companies as well as public organizations. However, most existing sentiment analysis techniques are only able to analyze data at the aggregate level, merely providing a binary classification (positive vs. negative), and are not able to generate finer characterizations of sentiments as well as emotions involved. This paper describes a new opinion analysis scheme, i.e., a multi-level fine-scaled sentiment sensing with ambivalence handling. The ambivalence handler is presented in detail along with the strength-level tune parameters for analyzing the strength and the fine-scale of both positive or negative sentiments. It is capable of drilling deeper into text in order to reveal multi-level fine-scaled sentiments as well as different types of emotions.
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Khanvilkar, Gayatri, and Prof Deepali Vora. "Sentiment Analysis for Product Recommendation Using Random Forest." International Journal of Engineering & Technology 7, no. 3.3 (June 21, 2018): 87. http://dx.doi.org/10.14419/ijet.v7i3.3.14492.
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Analysis of sentiments is to analyze the natural language and to find the emotions, express by the human beings. The idea behind sentiment analysis is to determine polarity of textual opinion given by person. Sentiment Analysis is useful in product recommendations. Based on the reviews given by the user; the products can be recommended to another user. Major product websites are using sentiment analysis to understand the popularity and problems with the product. Sentiment analysis mainly formulated as two class classification problem, positive and negative. Sentiment analysis using ordinal classification gives more clear idea about sentiments. The proposed system determines polarity of reviews given by users, using ordinal classification. The system will give polarity using machine learning algorithms SVM and Random Forest. The achieved polarity will be used to provide recommendation to users.
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Lappeman, James, Keneilwe Munyai, and Benjamin Mugo Kagina. "Negative sentiment towards COVID-19 vaccines: A comparative study of USA and UK social media posts before vaccination rollout." F1000Research 10 (June 15, 2021): 472. http://dx.doi.org/10.12688/f1000research.52061.1.
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Abstract Introduction: The global spread of the COVID-19 pandemic was rapid and devastating to humanity. The public health response to the pandemic was rapid too. Completion of COVID-19 vaccine development was achieved in under a year. The USA and the UK were the first countries to rollout COVID-19 vaccines to contain the pandemic. Successful rollout of the vaccines hinges on many factors, among which is public trust. Aim: To investigate the sentiments towards COVID-19 vaccines in the USA and UK prior to vaccination rollout. Methods: Neuro-linguistic programming with human validation was used to analyse a sample of 243,883 COVID-19 vaccine related social media posts from the USA and the UK in the period 28 July to 28 August 2020. The sentiment analysis measured polarity (positive, neutral, negative), and the themes present in negative comments. Results: In the sample of 243,883 social media posts, both the USA and the UK had a net sentiment profile of approximately 28% positive, 8% negative and 63% neutral sentiment. On further analysis, there were distinct differences between the two country’s social media sentiment towards COVID-19 vaccines. The differences were seen in the themes behind the negative sentiment. In the USA, the negative sentiments were mainly due to health and safety concerns, the fear of making a vaccine mandatory, and the role that pharmaceutical companies would play with the release of vaccines. In the UK the main driver of negative sentiment was the fear of making the vaccine mandatory (almost double the size of the sentiment in the USA). Conclusions: Negative sentiments towards COVID-19 vaccines were prevalent in the third quarter of 2020 in the USA and the UK. Reasons behind the negative sentiments can be used by authorities in the two countries to design evidence-based interventions to address the refusal of vaccination against COVID-19.
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Hjerm, M. "Reconstructing “Positive” Nationalism: Evidence from Norway and Sweden." Sociological Research Online 3, no. 2 (June 1998): 21–35. http://dx.doi.org/10.5153/sro.163.
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This article sets out to compare nationalism or nationalist sentiment in the two neighboring countries of Norway and Sweden, since it has been claimed that nationalism differs both with respect to the degree of nationalism and the connotations it has in these two countries. In spite of the claimed differences between the two countries, this article shows that Norwegians and Swedes have to a similar extent nationalist sentiments and that xenophobia and protectionism follow in the footsteps of such attitudes in both the examined countries, indicating the negative sides of nationalism. Moreover, the two countries also show similar patterns regarding which groups in society that are most inclined to show nationalist sentiments.
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Mukta, Md Saddam Hossain, Md Adnanul Islam, Faisal Ahamed Khan, Afjal Hossain, Shuvanon Razik, Shazzad Hossain, and Jalal Mahmud. "A Comprehensive Guideline for Bengali Sentiment Annotation." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 2 (March 31, 2022): 1–19. http://dx.doi.org/10.1145/3474363.
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Sentiment Analysis (SA) is a Natural Language Processing (NLP) and an Information Extraction (IE) task that primarily aims to obtain the writer’s feelings expressed in positive or negative by analyzing a large number of documents. SA is also widely studied in the fields of data mining, web mining, text mining, and information retrieval. The fundamental task in sentiment analysis is to classify the polarity of a given content as Positive, Negative, or Neutral . Although extensive research has been conducted in this area of computational linguistics, most of the research work has been carried out in the context of English language. However, Bengali sentiment expression has varying degree of sentiment labels, which can be plausibly distinct from English language. Therefore, sentiment assessment of Bengali language is undeniably important to be developed and executed properly. In sentiment analysis, the prediction potential of an automatic modeling is completely dependent on the quality of dataset annotation. Bengali sentiment annotation is a challenging task due to diversified structures (syntax) of the language and its different degrees of innate sentiments (i.e., weakly and strongly positive/negative sentiments). Thus, in this article, we propose a novel and precise guideline for the researchers, linguistic experts, and referees to annotate Bengali sentences immaculately with a view to building effective datasets for automatic sentiment prediction efficiently.
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Waworundeng, Jacquline Morlav S., Green Arther Sandag, Reynoldus Andrias Sahulata, and Godlife Davidson Rellely. "Sentiment Analysis of Online Lectures Tweets using Naïve Bayes Classifier." CogITo Smart Journal 8, no. 2 (December 21, 2022): 371–84. http://dx.doi.org/10.31154/cogito.v8i2.414.371-384.
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Online lecture is an alternative learning method during the Covid-19 pandemic. There are opinions with pro and contra of the learning method. The purpose of this study is to evaluate the tweets of opinion or sentiment retrieved from social media Twitter regarding online lectures among the Indonesian community. Twint is used to collect the data tweet and Jupyter notebook is for text preprocessing and classification. The processes started with scraping data from Twitter, text preprocessing, and text classification. Using the Naïve Bayes classifier shows the performance has a precision value of 100%, an accuracy value of 70.8%, an F-measure of 10.2%, and a recall value of 5.4%. Performance rating can be affected by the dataset used for modeling. This analysis covers the positive sentiment and negative sentiments toward online lectures and the result shows 69% negative sentiments and 31% positive sentiments. The negative sentiments had a higher percentage compared to positive sentiments. The results were also supported by the word cloud which expressed a high frequency of negative words such as sleep problems, bored, tired, dizzy, difficult and lazy. So, it is concluded that during the Covid-19 pandemic from August 1, 2020, to May 31, 2021, Twitter users in Indonesia had negative sentiments about online lectures.
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Liu, Zhi, Wenjing Zhang, Hercy N. H. Cheng, Jianwen Sun, and Sannyuya Liu. "Investigating Relationship Between Discourse Behavioral Patterns and Academic Achievements of Students in SPOC Discussion Forum." International Journal of Distance Education Technologies 16, no. 2 (April 2018): 37–50. http://dx.doi.org/10.4018/ijdet.2018040103.
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As an overt expression of internal mental processes, discourses have become one main data source for the research of interactive learning. To deeply explore behavioral regularities among interactions, this article firstly adopts the content analysis method to summarize students' engagement patterns within a course forum in a small private online course (SPOC) system. Secondly, through sentiment word matching and sentiment density calculation, the authors characterize the evolution trends of collective positive and negative sentiments, and compare sentiment strengths of different achieving students. The analytical result shows that there is a significant correlation between most engagement patterns and academic achievements, and high-achieving group seems more active than low-achieving group in terms of interactive, register, question, viewpoint and thematic postings. Besides, both of high and middle-achieving students are superior to low-achieving students on positive sentiment. But, there is no significant difference among high-, middle- and low-achieving students on negative sentiments.
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Yasa, I. Gede Cahya Purnama, Ngurah Agus Sanjaya ER, and Luh Arida Ayu Rahning Putri. "Sentiment Analysis of Snack Review Using the Naïve Bayes Method." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 8, no. 3 (January 25, 2020): 333. http://dx.doi.org/10.24843/jlk.2020.v08.i03.p16.
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Fast food is a product that we often encounter in stores such as convenience stores. Ready-to-eat products can now be easily found by consumers. One of the reason is due to the expansion of minimarkets in areas that are easily reached, such as housing complexes, school areas, and offices. Sentiment analysis is used to determine whether an opinion or comment on a product has a positive or negative interest and can be used as a reference in improving service, or improving product quality. In this research, we study the sentiments of consumers towards snack food products as a reference to improve the level of service and quality of these products..
We classify the sentiment of a review on snack food products as positive and negative. To classify the sentiments we apply the Naïve Bayes and Multinomial Naïve Bayes methods. We compare the two methods to study the most effective and efficient method for classifying sentiments on reviews of snack food products.
Keywords: Sentiment Analysis, TF-IDF, Naïve Bayes,Multinomial, Review, Snack, Preprocessing
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Hermanto, Dedi Tri, Arief Setyanto, and Emha Taufiq Luthfi. "Algoritma LSTM-CNN untuk Binary Klasifikasi dengan Word2vec pada Media Online." Creative Information Technology Journal 8, no. 1 (March 31, 2021): 64. http://dx.doi.org/10.24076/citec.2021v8i1.264.
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Media online banyak menghasilkan berbagai macam berita, baik ekonomi, politik, kesehatan, olahraga atau ilmu pengetahuan. Di antara itu semua, ekonomi adalah salah satu topik menarik untuk dibahas. Ekonomi memiliki dampak langsung kepada warga negara, perusahaan, bahkan pasar tradisional tergantung pada kondisi ekonomi di suatu negara. Sentimen yang terkandung dalam berita dapat mempengaruhi pandangan masyarakat terhadap suatu hal atau kebijakan pemerintah. Topik ekonomi adalah bahasan yang menarik untuk dilakukan penelitian karena memiliki dampak langsung kepada masyarakat Indonesia. Namun, masih sedikit penelitian yang menerapkan metode deep learning yaitu Long Short-Term Memory dan CNN untuk analisis sentimen pada artikel finance di Indonesia. Penelitian ini bertujuan untuk melakukan pengklasifikasian judul berita berbahasa Indonesia berdasarkan sentimen positif, negatif dengan menggunakan metode LSTM, LSTM-CNN, CNN-LSTM. Dataset yang digunakan adalah data judul artikel berbahasa Indonesia yang diambil dari situs Detik Finance. Berdasarkan hasil pengujian memperlihatkan bahwa metode LSTM, LSTM-CNN, CNN-LSTM memiliki hasil akurasi sebesar, 62%, 65% dan 74%.Kata Kunci — LSTM, sentiment analysis, CNNOnline media produce a lot of various kinds of news, be it economics, politics, health, sports or science. Among them, economics is one interesting topic to discuss. The economy has a direct impact on citizens, companies, and even traditional markets depending on the economic conditions in a country. The sentiment contained in the news can influence people's views on a matter or government policy. The topic of economics is an interesting topic for research because it has a direct impact on Indonesian society. However, there are still few studies that apply deep learning methods, namely Long Short-Term Memory and CNN for sentiment analysis on finance articles in Indonesia. This study aims to classify Indonesian news headlines based on positive and negative sentiments using the LSTM, LSTM-CNN, CNN-LSTM methods. The dataset used is data on Indonesian language article titles taken from the Detik Finance website. Based on the test results, it shows that the LSTM, LSTM-CNN, CNN-LSTM methods have an accuracy of, 62%, 65% and 74%.Keywords — LSTM, sentiment analysis, CNN
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Khalida, Rakhmi, and Siti Setiawati. "Analisis Sentimen Sistem E-Tilang Menggunakan Algoritma Naive Bayes Dengan Optimalisasi Information Gain." Journal of Informatic and Information Security 1, no. 1 (May 29, 2020): 19–26. http://dx.doi.org/10.31599/jiforty.v1i1.137.
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Abstract
The Government of Indonesia took steps to change the system to improve public services in traffic violations by implementing the e-ticketing system. This system is a solution for disciplining motorized motorists from committing traffic violations. The existence of e-ticketing is also a solution to prevent the delinquency of law enforcers from illegal levies, peace terms in place, to accountability of fines. In this study, sentiment analysis of the e-ticketing system or opinion mining to classify the variety of public comments that give a positive, negative or neutral impression. Twitter social media is one of the objects to express opinions because it is user friendly, updated topics, and openly accesses tweets. Opinions on Twitter are collected, then the preprocessing stage is performed, then the selection of information gain features helps reduce noise caused by irrelevant labels, the next step is the classification of sentiments with the Naïve Bayes algorithm and finally polarity sentiments. This research resulted in an accuracy of 41.82%, a precision of 50.51% and a recall of 45.45%.
Keywords: Sentiment analysis, E-ticketing, Information Gain, Naive Bayes
Abstrak
Pemerintah Indonesia melakukan langkah perubahan untuk memperbaiki sistem pelayanan publik dalam pelanggaran berlalu-lintas yaitu dengan menerapkan sistem e-Tilang. Sistem ini menjadi solusi mendisiplinkan para pengendara kendaraan bermotor dari banyaknya melakukan pelanggaran berlalu-lintas. Keberadaan e-Tilang juga menjadi solusi mencegah kenakalan penegak hukum dari pungutan liar, istilah damai ditempat, hingga akuntabilitas uang denda. Dalam penelitian ini melakukan analisis sentimen tentang sistem e-Tilang atau opinion mining untuk mengelompokan ragam komentar masyarakat yang memberikan kesan positif, negatif atau netral. Media sosial Twitter menjadi salah satu objek untuk menyampaikan opini karena user friendly, topik ter-update, dan terbuka mengakses tweet. Opini pada twitter dikumpulkan, lalu dilakukan tahapan preprocessing, selanjutnya dengan seleksi fitur information gain membantu mengurangi noise yang disebabkan oleh label-label yang tidak relevan, tahap selanjutnya adalah klasifikasi sentimen dengan algoritma Naïve Bayes dan terakhir sentimen polarity. Penelitian ini menghasilkan accuracy 41,82%, presisi 50,51% dan recall 45,45%.
Kata kunci: Analisis sentimen, E-Tilang, Information Gain, Naive Bayes
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Intan, Oka, and Sri Widiyanesti. "Sentiment Analysis of West Java International Airport (Bijb) Kertajati on Twitter." Almana : Jurnal Manajemen dan Bisnis 4, no. 2 (August 10, 2020): 176–82. http://dx.doi.org/10.36555/almana.v4i2.1348.
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The rapid development of technology allows everything to accessed by the internet that causes many users of social media and one of the social media is Twitter. An interesting topic to discuss on Twitter is about new and fresh things that attract many users to get involved. One of the things that attract Twitter users is the construction of a new airport, namely Kertajati Airport, which has some problems with airport activities, such as the small number of visitors, lonely conditions of the airport, and decreased number of routes. This study aims to find out Twitter user sentiments towards Kertajati Airport in West Java to know the quality of Kertajati Airport. The method used in this study is sentiment analysis by looking at the calculation of how many positive and negative sentiment have been obtained with the most result so it can reflect the quality of Kertajati Airport and then there is a word cloud to see the spread of word related to sentiment. The results of this study indicate that the quality of the Kertajati Airport cannot be said to be good because the results of the sentiment analysis found that negative sentiments have more percentages than positive sentiments
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Duan, Sutian, Zhiyong Shen, and Xiao Luo. "Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments." International Journal of Environmental Research and Public Health 19, no. 8 (April 15, 2022): 4794. http://dx.doi.org/10.3390/ijerph19084794.
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As the relationship between the built environment and the sense of human experience becomes increasingly important, emotional geography has begun to focus on sentiments in space and time and improving the quality of urban construction from the perspective of public emotion and mental health. While youth is a powerful force in urban construction, there are no studies on the relationship between urban youth sentiments and the built environment. With the development of the Internet, social media has provided a large source of data for the metrics of youth sentiment. Based on data from more than 10,000 geolocated Sina Weibo comments posted over one week (from 19 to 25 July 2021) in Shanghai and using a machine learning algorithm for attention mechanism, this study calculates the sentiment label and sentiment intensity of each comment. Ten elements in five aspects were selected to assess the built environment at different scales and also to explore the correlations between built environment elements and sentiment intensity at different scales. The study finds that the overall sentiment of Shanghai youth tends to be negative. Sentiment intensity is significantly associated with most built environment elements at smaller scales. Urban youth have a higher proportion of both happy and sad sentiments, within which sad sentiments are more closely related to the built environment and are significantly related to all built environment elements. This study uses a deep learning algorithm to improve the accuracy of sentiment classification and confirms that the built environment has a great impact on sentiment. This research can help cities develop built environment optimization measures and policies to create positive emotional environments and enhance the well-being of urban youth.
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Saeed, Sadia, Tehseen Zahra, and Asim Ali Fayyaz. "Sentiment Analysis of Imran Khan’s Tweets." Volume 36, Issue 3 36, no. 3 (September 30, 2021): 473–94. http://dx.doi.org/10.33824/pjpr.2021.36.3.26.
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In the recent past, sentiment analysis has been an area of interests of psychologists, sociologists, neurologists, computer scientists, and linguists including corpus linguists and computational linguists. Interdisciplinary approaches to researching various issues especially the analysis of social media websites such as Facebook, Twitter, and Instagram are becoming popular nowadays. The availability of data on social media has made it easier to analyse the opinion or sentiments of its users. Analysis of these sentiments could reveal the face of users and it could help in various decision-making processes. Sentiment analysis is a system of knowing polarity (positive, negative, and neutral) in discourse. Moreover, sentiments can enable and disable certain functions of discourse and can divert the attention of the audience from important to a less important issue or otherwise, hence, there is a need to analyse the sentiments. In this research, sentiments (Polarity) of Imran Khan’s tweets are analysed with the help of R studio. Data for this study is collected from Imran Khan’s one-year’s tweets, tweeted from 1st January 2018 to 20th November 2018. Later we saved the data in. csv files. The results of the polarity check revealed that he has used all three types of sentiments that is positive, negative, and neutral. However, he mostly used neutral or free polarity items (FPIs) that is 67.41% in his tweets. Among positive and negative polarity items the number of negative polarity items (NPIs) is higher that is 23.21% as compared to positive polarity items (PPIs) which are only 9.40%. The manual analysis of results revealed that only software is not enough and there is a need to check the accuracy of the results manually. The use of negative polarity/negative face reveals that he tries to be independent and autonomous in his decisions (Goffman, 1967). The use of positive polarity items shows he tries to show his positive face to others. Moreover, sentiment analysis demonstrates the presence of themes propagated through the use of various lexical items.
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Thaha, Abdurrahman Rahim. "Sentiment Analysis of University Libraries during the Covid-19 Pandemic in Indonesia." Daengku: Journal of Humanities and Social Sciences Innovation 2, no. 5 (October 3, 2022): 626–31. http://dx.doi.org/10.35877/454ri.daengku1215.
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The purpose of this study was to analyze the sentiments of university library visitors during the pandemic. Data collection techniques using web scraping techniques using a data scraper application. The data is taken from Google reviews as many as 261 reviews from ten universities in Indonesia. Data analysis technique using Vader method for sentiment analysis and Ekman method for classification of emotional sentiment. The results of the sentiment analysis in this study show that visitor satisfaction is quite high at the university library during the pandemic. Positive sentiment in the university library is 80.8% and the classification of emotional sentiment is dominated by joy as much as 71.6% compared to other sentiments such as negative, fear, and sadness. These results indicate that visits to the university library can still be carried out well even though there are many limitations in activities in the library because of the pandemic.
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Farkhod, Akhmedov, Akmalbek Abdusalomov, Fazliddin Makhmudov, and Young Im Cho. "LDA-Based Topic Modeling Sentiment Analysis Using Topic/Document/Sentence (TDS) Model." Applied Sciences 11, no. 23 (November 23, 2021): 11091. http://dx.doi.org/10.3390/app112311091.
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Customer reviews on the Internet reflect users’ sentiments about the product, service, and social events. As sentiments can be divided into positive, negative, and neutral forms, sentiment analysis processes identify the polarity of information in the source materials toward an entity. Most studies have focused on document-level sentiment classification. In this study, we apply an unsupervised machine learning approach to discover sentiment polarity not only at the document level but also at the word level. The proposed topic document sentence (TDS) model is based on joint sentiment topic (JST) and latent Dirichlet allocation (LDA) topic modeling techniques. The IMDB dataset, comprising user reviews, was used for data analysis. First, we applied the LDA model to discover topics from the reviews; then, the TDS model was implemented to identify the polarity of the sentiment from topic to document, and from document to word levels. The LDAvis tool was used for data visualization. The experimental results show that the analysis not only obtained good topic partitioning results, but also achieved high sentiment analysis accuracy in document- and word-level sentiment classifications.
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Nugroho, Dimas Dwi, Arief Setyanto, and Hanif Al Fatta. "Analisis Sentimen Sekolah Online pada Twitter dengan Algoritma Support Vector Machine." Respati 17, no. 3 (November 10, 2022): 38. http://dx.doi.org/10.35842/jtir.v17i3.466.
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INTISARISeiring meningkatnya masyarakat yang terdampak wabah Covid-19, pemerintah akhirnya melakukan berbagai kebijakan untuk mengurangi resiko dari wabah Covid-19, salah satunya adalah Kebijakan Sekolah Online (Belajar dari rumah). Namun menteri Pendidikan dan Kebudayaan Nadiem Makarim juga mewacanakan bahwa PJJ (Pelajaran Jarak Jauh) tetap dilakukan setelah pandemi Covid-19 sudah selesai. Dari kebijakan tersebut menimbulkan berbagai opini positif dan negatif dari masyarakat, opini tersebut dapat dilihat melalui media sosial twitter. Sentimen dan opini adalah fitur penting dari keberadaan manusia. Analisis Sentimen bermaksud untuk memahami pendapat-pendapat ini dan mendistribusikannya ke dalam kategori seperti positif, netral dan negatif. Analisis sentiment saat ini terus berkembang dengan berbagai methode dan algoritma yang ada. Berdasarkan beberapa penelitian yang ada diketahui bahwa dengan menggunakan metode Algoritma Support Vector Machine dapat memberikan hasil akurasi yang lebih baik dari pada Algoritma yang lain.. Hasil penelitian dari 1200 Data tweet diperoleh Jumlah tweet netral sebanyak 445, tweet positif sebanyak 396 dan tweet negatif sebanyak 359 tweet. Dari data tersebut kemudian diproses menggunakan algoritma Support Vector Machine dan mendapatkan hasil nilai accuracy sebesar 82%, nilai Precision 83%, nilai Recall 82% dan nilai F1-Score 82 %., maka dapat disimpulkan metode Algoritma Support Vector Machine (SVM) dinilai lebih relevan untuk diterapkan pada penelitian sentiment analisis.Kata kunci— Sentimen Analisis, SVM, Covid-19, Sekolah Online, Scrawling Twitter. ABSTRACTAlong with the increasing number of people affected by the Covid-19 outbreak, the government has finally implemented various policies to reduce the risk of the Covid-19 outbreak, one of which is the Online School Policy (Learning from home). However, the Minister of Education and Culture Nadiem Makarim also discoursed that PJJ (Distance Learning) would still be carried out after the Covid-19 pandemic was over. From this policy, it raises various positive and negative opinions from the public, these opinions can be seen through Twitter social media. Sentiments and opinions are essential features of human existence. Sentiment Analysis intends to understand these opinions and distribute them into categories such as positive, neutral and negative. Sentiment analysis is currently growing with various existing methods and algorithms. Based on several existing studies, it is known that using the Support Vector Machine Algorithm method can provide better accuracy results than other algorithms. The results of the 1200 tweet data obtained were 445 neutral tweets, 396 positive tweets and 359 negative tweets. tweets. From this data, it is processed using the Support Vector Machine algorithm and gets an accuracy value of 82%, Precision value 83%, Recall value 82% and F1-Score value 82%., it can be concluded that the Support Vector Machine (SVM) Algorithm method is considered more relevant to be applied to sentiment analysis research..Kata kunci— Analysis Sentiment, SVM, Covid-19, Online School, Scrawling Twitter.
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Muppidi, Satish, Satya Keerthi Gorripati, and B. Kishore. "An approach for bibliographic citation sentiment analysis using deep learning." International Journal of Knowledge-based and Intelligent Engineering Systems 24, no. 4 (January 18, 2021): 353–62. http://dx.doi.org/10.3233/kes-200087.
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Sentiment analysis of scientific citations is a novel and remarkable research area. Most of the work on opinion or sentiment analysis has been suggested on social platforms such as Blogs, Twitter, and Facebook. Nevertheless, when it comes to recognizing sentiments from scientific citation papers, investigators used to face difficulties due to the implied and unseen natures of sentiments or opinions. As the citation references are reflected implicitly positive in opinion, famous ranking and indexing prototypes frequently disregard the sentiment existence while citing. Hence, in the proposed framework the paper emphasizes the issue of classifying positive and negative polarity of reference sentiments in scientific research papers. First, the paper scraps the PDF articles from arxiv.org under the computer science group consisting of articles that are comprised of ‘autism’ in their title, then the paper extracted cited references and assigns polarity scores to each cited reference. The paper uses a supervised classifier with a combination of significant feature sets and compared the performance of the models. Experimental results show that a combined CNN-LSTM deep neural network model results in 85% of accuracy while traditional models result in less accuracy.
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Misopoulos, Fotis, Miljana Mitic, Alexandros Kapoulas, and Christos Karapiperis. "Uncovering customer service experiences with Twitter: the case of airline industry." Management Decision 52, no. 4 (May 13, 2014): 705–23. http://dx.doi.org/10.1108/md-03-2012-0235.
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Purpose – In this paper the authors present a study that uses Twitter to identify critical elements of customer service in the airline industry. The goal of the study was to uncover customer opinions about services by monitoring and analyzing public Twitter commentaries. The purpose of this paper is to identify elements of customer service that provide positive experiences to customers as well as to identify service processed and features that require further improvements. Design/methodology/approach – The authors employed the approach of sentiment analysis as part of the netnography study. The authors processed 67,953 publicly shared tweets to identify customer sentiments about services of four airline companies. Sentiment analysis was conducted using the lexicon approach and vector-space model for assessing the polarity of Twitter posts. Findings – By analyzing Twitter posts for their sentiment polarity the authors were able to identify areas of customer service that caused customer satisfaction, dissatisfaction as well as delight. Positive sentiments were linked mostly to online and mobile check-in services, favorable prices, and flight experiences. Negative sentiments revealed problems with usability of companies’ web sites, flight delays and lost luggage. Evidence of delightful experiences was recorded among services provided in airport lounges. Originality/value – Paper demonstrates how sentiment analysis of Twitter feeds can be used in research on customer service experiences, as an alternative to Kano and SERVQUAL models.
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Amelia, Resti, Darmansah Darmansah, Nanda Sesty Prastiwi, and Muhammad Eka Purbaya. "Impementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Masyarakat Indonesia Mengenai Drama Korea Pada Twitter." JURIKOM (Jurnal Riset Komputer) 9, no. 2 (April 29, 2022): 338. http://dx.doi.org/10.30865/jurikom.v9i2.3895.
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Korean culture in the last two decades has shaken the whole world, including Indonesia, almost all millennial children talk about Korean culture, whether from dramas, films, songs, fashion, lifestyle, industrial products have begun to penetrate the lives of Indonesian people. One of the trends of public interest is Korean film drama. However, in the expansion of Korean film dramas in Indonesia, there must still be a negative perspective, so an analysis of the sentiments of Indonesian people's opinions regarding Korean culture is currently on the rise. This sentiment analysis data is taken from comments about Korean dramas that are widely written on Twitter. From the many opinions about Korean dramas, a classification is needed according to the existing sentiments so that it will be easy to get the tendency of opinions written on Twitter towards Korean dramas whether they tend to have positive, neutral or negative opinions. In the analysis of opinion sentiment using a Naive Bayes approach taken from Twitter Social Media. The application of the Nave Bayes algorithm in grouping positive, neutral and negative sentiments based on Korean drama commentary review data collected. The purpose of this study is to analyze public opinion and sentiment on Korean dramas on Twitter based on 100 data taken, using Orange tools for the sentiment analysis process. The results given show a percentage value of 69% by calculating the Naive Bayes algorithm
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Huangfu, Luwen, Yiwen Mo, Peijie Zhang, Daniel Dajun Zeng, and Saike He. "COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment–Based Topic Modeling." Journal of Medical Internet Research 24, no. 2 (February 8, 2022): e31726. http://dx.doi.org/10.2196/31726.
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Background COVID-19 vaccines are one of the most effective preventive strategies for containing the pandemic. Having a better understanding of the public’s conceptions of COVID-19 vaccines may aid in the effort to promptly and thoroughly vaccinate the community. However, because no empirical research has yet fully explored the public’s vaccine awareness through sentiment–based topic modeling, little is known about the evolution of public attitude since the rollout of COVID-19 vaccines. Objective In this study, we specifically focused on tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, and Johnson & Johnson) after vaccines became publicly available. We aimed to explore the overall sentiments and topics of tweets about COVID-19 vaccines, as well as how such sentiments and main concerns evolved. Methods We collected 1,122,139 tweets related to COVID-19 vaccines from December 14, 2020, to April 30, 2021, using Twitter’s application programming interface. We removed retweets and duplicate tweets to avoid data redundancy, which resulted in 857,128 tweets. We then applied sentiment–based topic modeling by using the compound score to determine sentiment polarity and the coherence score to determine the optimal topic number for different sentiment polarity categories. Finally, we calculated the topic distribution to illustrate the topic evolution of main concerns. Results Overall, 398,661 (46.51%) were positive, 204,084 (23.81%) were negative, 245,976 (28.70%) were neutral, 6899 (0.80%) were highly positive, and 1508 (0.18%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251,979/405,560, 62.13%), getting vaccination (76,029/405,560, 18.75%), and vaccine information and knowledge (21,127/405,560, 5.21%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115,206/205,592, 56.04%), extreme side effects of the vaccines (19,690/205,592, 9.58%), and vaccine supply and rollout (17,154/205,592, 8.34%). During the study period, negative sentiment trends were stable, while positive sentiments could be easily influenced. Topic heatmap visualization demonstrated how main concerns changed during the current widespread vaccination campaign. Conclusions To the best of our knowledge, this is the first study to evaluate public COVID-19 vaccine awareness and awareness trends on social media with automated sentiment–based topic modeling after vaccine rollout. Our results can help policymakers and research communities track public attitudes toward COVID-19 vaccines and help them make decisions to promote the vaccination campaign.
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Misra, Shashwat, Jasleen Kaur, and U. M. Prakash. "Sentimental Analysis using Machine Learning and Deep Learning: Performance Measurement, Challenges and Opportunities." International Journal of Current Engineering and Technology 11, no. 04 (August 4, 2021): 412–17. http://dx.doi.org/10.14741/ijcet/v.11.4.3.
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Our regular existence has consistently been impacted with the aid of what individuals think. Thoughts and tests of others have consistently inspired our personal sentiments. Web 2.0 has caused extended action in Podcasting, Tagging, Blogging, and Social Networking. As an end result, social media web sites have emerged as one of the structures to raise consumer’s opinions and influence the way any commercial enterprise is commercialized. Sentiment analysis is the prediction of feelings in a word, sentence, or corpus of files. It is deliberate to fill in as a software to recognize the mentalities, conclusions, and feelings communicated interior a web point out. This paper reviews at the design of sentiment evaluation, mining the sizeable resources of information for evaluations. The number one goal is to provide a way for studying sentiment rating in social media platforms. Here we discuss diverse methods to perform a computational remedy of sentiments and reviews, diverse supervised or facts-driven techniques to research sentiments like Naïve Bayes, Support Vector Machine, and SentiWordNet technique to Sentiment Analysis. Results classify consumer’s belief through social media posts into positive, negative, and neutral.