Journal articles on the topic 'Sentiment analytics'
To see the other types of publications on this topic, follow the link: Sentiment analytics.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Sentiment analytics.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Rokade, Prakash P., and Aruna Kumari D. "Business intelligence analytics using sentiment analysis-a survey." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 1 (February 1, 2019): 613. http://dx.doi.org/10.11591/ijece.v9i1.pp613-620.
Full textAbstract:
Sentiment analysis (SA) is the study and analysis of sentiments, appraisals and impressions by people about entities, person, happening, topics and services. SA uses text analysis techniques and natural language processing methods to locate and extract information from big data. As most of the people are networked themselves through social websites, they use to express their sentiments through these websites.These sentiments are proved fruitful to an individual, business, government for making decisions. The impressions posted on different available sources are being used by organization to know the market mood about the services they are providing. Analyzing huge moods expressed with different features, style have raised challenge for users. This paper focuses on understanding the fundamentals of sentiment analysis, the techniques used for sentiment extraction and analysis. These techniques are then compared for accuracy, advantages and limitations. Based on the accuracy for expexted approach, we may use the suitable technique.
2
BAKİROV, Aslan, Kevser Nur ÇOĞALMIŞ, and Ahmet BULUT. "Scalable sentiment analytics." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 1560–70. http://dx.doi.org/10.3906/elk-1311-128.
Full text
3
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.
Full textAbstract:
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.
4
Ali, G. G. Md Nawaz, Md Mokhlesur Rahman, Md Amjad Hossain, Md Shahinoor Rahman, Kamal Chandra Paul, Jean-Claude Thill, and Jim Samuel. "Public Perceptions of COVID-19 Vaccines: Policy Implications from US Spatiotemporal Sentiment Analytics." Healthcare 9, no. 9 (August 27, 2021): 1110. http://dx.doi.org/10.3390/healthcare9091110.
Full textAbstract:
There is a compelling and pressing need to better understand the temporal dynamics of public sentiment towards COVID-19 vaccines in the US on a national and state-wise level for facilitating appropriate public policy applications. Our analysis of social media data from early February and late March 2021 shows that, despite the overall strength of positive sentiment and despite the increasing numbers of Americans being fully vaccinated, negative sentiment towards COVID-19 vaccines still persists among segments of people who are hesitant towards the vaccine. In this study, we perform sentiment analytics on vaccine tweets, monitor changes in public sentiment over time, contrast vaccination sentiment scores with actual vaccination data from the US CDC and the Household Pulse Survey (HPS), explore the influence of maturity of Twitter user-accounts and generate geographic mapping of tweet sentiments. We observe that fear sentiment remained unchanged in populous states, whereas trust sentiment declined slightly in these same states. Changes in sentiments were more notable among less populous states in the central sections of the US. Furthermore, we leverage the emotion polarity based Public Sentiment Scenarios (PSS) framework, which was developed for COVID-19 sentiment analytics, to systematically posit implications for public policy processes with the aim of improving the positioning, messaging, and administration of vaccines. These insights are expected to contribute to policies that can expedite the vaccination program and move the nation closer to the cherished herd immunity goal.
5
Rokade, Prakash Pandharinath, and Aruna Kumari D. "Business recommendation based on collaborative filtering and feature engineering – aproposed approach." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 2614. http://dx.doi.org/10.11591/ijece.v9i4.pp2614-2619.
Full textAbstract:
Business decisions for any service or product depend on sentiments by people. We get these sentiments or rating on social websites like twitter, kaggle. The mood of people towards any event, service and product are expressed in these sentiments or rating. The text of sentiment contains different linguistic features of sentence. A sentiment sentence also contains other features which are playing a vital role in deciding the polarity of sentiments. If features selection is proper one can extract better sentiments for decision making. A directed preprocessing will feed filtered input to any machine learning approach. Feature based collaborative filtering can be used for better sentiment analysis. Better use of parts of speech (POS) followed by guided preprocessing and evaluation will minimize error for sentiment polarity and hence the better recommendation to the user for business analytics can be attained.
6
Gukanesh, A. V., G. Karthick Kumar, and K. Karthik Raja Kumar N. Saranya. "Twitter Data Analytics – Sentiment Analysis of An Election." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1600–1603. http://dx.doi.org/10.31142/ijtsrd11457.
Full text
7
Raman, Ramakrishnan, Sandeep Bhattacharya, and Dhanya Pramod. "Predict employee attrition by using predictive analytics." Benchmarking: An International Journal 26, no. 1 (February 4, 2019): 2–18. http://dx.doi.org/10.1108/bij-03-2018-0083.
Full textAbstract:
PurposeResearch questions that this paper attempts to answer are – do the features in general email communication have any significance to a teaching faculty member leaving the business school? Do the sentiments expressed in email communication have any significance to a teaching faculty member leaving the business school? Do the stages mentioned in the transtheoretical model have any relevance to the email behaviour of an individual when he or she goes through the decision process leading to the decision to quit? The purpose of this paper is to study email patterns and use predictive analytics to correlate with the real-world situation of leaving the business school.Design/methodology/approachThe email repository (2010–2017) of 126 teaching faculty members who were associated with a business school as full-time faculty members is the data set that was used for the research. Of the 126 teaching faculty members, 42 had left the business school during this time frame. Correlation analysis, word count analysis and sentiment analysis were executed using “R” programming, and sentiment “R” package was used to understand the sentiment and its association in leaving the business school. From the email repository, a rich feature set of data was extracted for correlation analysis to discover the features which had strong correlation with the faculty member leaving the business school. The research also used data-logging tools to extract aggregated statistics for word frequency counts and sentiment features.FindingsThose faculty members who decide to leave are involved more in external communication and less in internal communications. Also, those who decide to leave initiate fewer email conversations and opt to forward emails to colleagues. Correlation analysis shows that negative sentiment goes down, as faculty members leave the organisation and this is in contrary to the existing review of literature. The research also shows that the triggering point or the intention to leave is positively correlated to the downward swing of the emotional valence (positive sentiment). A number of email features have shown change in patterns which are correlated to a faculty member quitting the business school.Research limitations/implicationsFaculty members of only one business school have been considered and this is primary due to cost, privacy and complexities involved in procuring and handling the data. Also, the reasons for exhibiting the sentiments and their root cause have not been studied. Also the designation, roles and responsibilities of faculty members have not been taken into consideration.Practical implicationsBusiness schools all over India always have a challenge to recruit good faculty members who can take up research activities, teach and also shoulder administrative responsibilities. Retaining faculty members and keeping attrition levels low will help business schools to maintain the standards of excellence that they aspire. This research is immensely useful for business school, which can use email analytics in predicting the intention of the faculty members leaving their business school.Originality/valueAlthough past studies have studied attrition, this study uses predictive analytics and maps it to the intention to quit. This study helps business schools to predict the chance of faculty members leaving the business school which is of immense value, as appropriate measures can be taken to retain and restrict attrition.
8
Singh, Amit, Mamata Jenamani, Jitesh Thakkar, and Yogesh K. Dwivedi. "A Text Analytics Framework for Performance Assessment and Weakness Detection From Online Reviews." Journal of Global Information Management 30, no. 8 (September 1, 2021): 1–26. http://dx.doi.org/10.4018/jgim.304069.
Full textAbstract:
Present research proposes a framework that integrates aspect-level sentiment analysis with multi-criteria decision making (TOPSIS) and control charts to uncover hidden quality patterns. While sentiment analysis quantifies consumer opinions corresponding to various product features, TOPSIS uses the sentiment scores to rank manufacturers based on their relative performance. Finally, U and P control charts assist in discovering the weak aspects and corresponding attributes. To extract aspect-level sentiments from reviews, we developed the ontology of passenger cars and designed a heuristic that connects the opinion-bearing texts to the exact automobile attribute. The proposed framework was applied to a review dataset collected from a well-known car portal in India. Considering five manufacturers from the mid-size car segment, we identified the weakest and discovered the aspects and attributes responsible for its perceived weakness.
9
Hao, Jin-Xing, Yu Fu, Cathy Hsu, Xiang (Robert) Li, and Nan Chen. "Introducing News Media Sentiment Analytics to Residents’ Attitudes Research." Journal of Travel Research 59, no. 8 (November 8, 2019): 1353–69. http://dx.doi.org/10.1177/0047287519884657.
Full textAbstract:
The progress in sentiment analytics and communication research provides a powerful scaffold by which to reexamine the long-debated research on residents’ attitudes toward tourism. To mitigate the limitations of the classical survey-based research method, this study takes a news media sentiment analytics perspective to unveil how the residents’ attitudes toward tourism evolve over time and how socioeconomic factors interact with such evolving attitudes in the context of Hong Kong. Drawn on a news data set containing 72,755 news articles published in Chinese language newspapers, this study computes the overall news sentiments for 156 calendar months since 2003, examines the face validity and nomological validity of the results, and discusses the long-run dynamics between residents’ attitudes and typical socioeconomic factors. This study adds a vital dimension to current residents’ attitudes research and practices from data-scarce to data-rich studies and from static snapshots to dynamic unfolding.
10
Fu, Yu, Jin-Xing Hao, Xiang (Robert) Li, and Cathy H. C. Hsu. "Predictive Accuracy of Sentiment Analytics for Tourism: A Metalearning Perspective on Chinese Travel News." Journal of Travel Research 58, no. 4 (May 16, 2018): 666–79. http://dx.doi.org/10.1177/0047287518772361.
Full textAbstract:
Sentiment analytics, as a computational method to extract emotion and detect polarity, has gained increasing attention in tourism research. However, issues regarding how to properly apply sentiment analytics are seldom addressed in the tourism literature. This study addresses such methodological challenges by employing the metalearning perspective to examine the design effects on predictive accuracy using a sentiment analysis experiment for Chinese travel news. Our results reveal strong interactions among key design factors of sentiment analytics on predictive accuracy; accordingly, this study formulates a metalearning framework to improve predictive accuracy for computational tourism research. Our study attempts to highlight and improve the methodological relevance and appropriateness of sentiment analytics for future tourism studies.
11
Balakrishnan, Vimala, Mohammed Kaity, Hajar A. Abdul Rahim, and Nazari Ismail. "SOCIAL MEDIA ANALYTICS USING SENTIMENT AND CONTENT ANALYSES ON THE 2018 MALAYSIA’S GENERAL ELECTION." Malaysian Journal of Computer Science 34, no. 2 (April 30, 2021): 171–83. http://dx.doi.org/10.22452/mjcs.vol34no2.3.
Full textAbstract:
This study analysed the political use of Twitter during the 2018 Malaysian General Election (GE14), using sentiment and content analyses to examine the patterns in online communication among urban Malaysians. Specifically, Naive Bayes, Support Vector Machine and Random Forest were used for sentiment analysis for the English tweets, with the results compared against two vectorization approaches. Content analysis involving human experts was used for the Malay tweets. Top trending hashtags were used to fetch tweets from April 15, 2018 to May 14, 2018, resulting in a curated corpus of 190 224 tweets. Naïve Bayes used along with Word2Vec outperformed all the other models with an accuracy of 63.7%, 66.8% and 64.9% for pre-GE14, GE14 and post-GE14, respectively. Generally, results indicate the majority of the sentiments to be positive in nature, followed by negative and neutral during pre-GE14, GE14 day and post-GE14 for the English speakers. Though similar sentiments were observed for the Malay speakers, the majority of their sentiments on election day were negative (i.e. 42%) as opposed to the English speakers (i.e. 31%).
12
Harfoushi, Osama, Dana Hasan, and Ruba Obiedat. "Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison." Modern Applied Science 12, no. 7 (June 21, 2018): 49. http://dx.doi.org/10.5539/mas.v12n7p49.
Full textAbstract:
The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals’ opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.
13
Ho, Ree Chan, Madusha Sandamali Withanage, and Kok Wei Khong. "Sentiment drivers of hotel customers: a hybrid approach using unstructured data from online reviews." Asia-Pacific Journal of Business Administration 12, no. 3/4 (August 3, 2020): 237–50. http://dx.doi.org/10.1108/apjba-09-2019-0192.
Full textAbstract:
PurposeWith the growth of social media and online communications, consumers are becoming more informed about hotels' services than ever before. They are writing online review to share their experiences, as well as reading online review before making a hotel reservation. Hotel customers considered it as reliable source and it influences customers' hotel selection. Most of these reviews reside in unstructured format, scattered across in the Internet and inherently unorganized. The purpose of this study was to use predictive text analytics to identify sentiment drivers from unstructured online reviews.Design/methodology/approachThe research used sentiment classifications to analyze customers' reviews on hotels from TripAdvisor. In total, 9,286 written reviews by hotel customers were scrapped from 442 hotels in Malaysia. A detailed text analytic was conducted and was followed by a development of a theoretical framework based on the hybrid approach. AMOS was used to analyze the relationship between customer sentiments and overall review rating.FindingsWith the use of Structural Equation Modeling (SEM) and clustering technique, a list of sentiment drivers was detected, i.e. location, room, service, sleep, value for money and cleanliness. Among these variables, service quality and room facilities emerged as the most influential factors. Sentiment drivers obtained in this study provided the insights to hotel operators to improve the hotel conditions.Research limitations/implicationsAlthough this study extended the existing literature on sentiment analysis by providing valuable insights to hoteliers, it is not without its limitations. For instance, online hotel reviews collected for this study were limited to one specific online review platform. Despite the large sample size to support and justify the findings, the generalizability power was restricted. Thus, future research should also consider and expand to other type of online review channels. Therefore, a need to examine these data reside various social media applications, i.e. Facebook, Instagram and YouTube.Practical implicationsThis study highlights the significance of hybrid predictive model in analyzing the unstructured hotel reviews. Based on the hybrid predictive model we developed, six sentiment drivers emerged from the data analysis, i.e. location, service quality, value for money, sleep quality, room design and cleanliness. This consideration is critical due to the ever-increasing unstructured data resides in the online space. This explores the possibility of applying data analytic technique in a more efficient manner to obtain customer insights for hotel managerial consideration.Originality/valueThis study analyzed customer sentiments toward the hotel in Malaysia with the use of predictive text analytics technique. The main contribution was the list of sentiment drivers and the insights needed to improve the hotel conditions in Malaysia. In addition, the findings demonstrated motivating findings from different methodological perspective and provided hoteliers with the recommendation for improved review ratings.
14
Shayaa, Shahid, Ainin Sulaiman, Arsalan Zahid Piprani, Mohammed Ali Al-Garadi, and Muhammad Ashraf. "Big Data Social Media Analytics for Purchasing Behaviour." International Journal of Engineering & Technology 7, no. 4.36 (December 9, 2018): 463. http://dx.doi.org/10.14419/ijet.v7i4.36.23917.
Full textAbstract:
The social media is rich in data and of late its data have been used for various types of analytics. This paper examines the purchasing behavior and sentiments of social media users from Jan - 2015 to Dec – 2016. The purchasing behaviour of the users is categorized into five: buy car, buy house, buy computer, buy hand phone and going for holiday. The paper will also demonstrate the trend of each individual category. The results of the analysis would provide businesses information on the social media users’ purchasing behavior, their sentiment thus allowing them to take more appropriate strategies to enhance their competitiveness.
15
Ghosh, Swarup Kr, Sowvik Dey, and Anupam Ghosh. "Knowledge Generation Using Sentiment Classification Involving Machine Learning on E-Commerce." International Journal of Business Analytics 6, no. 2 (April 2019): 74–90. http://dx.doi.org/10.4018/ijban.2019040104.
Full textAbstract:
Sentiment analysis manages the computational treatment of conclusion, notion, and content subjectivity. In this article, three sentiment classes such as positive, negative and neutral emotions have been demonstrated by appropriate features from raw unstructured data followed by data preprocessing steps. Applying best in class social analytics methodology to examine the sentiments embedded with purchaser remarks, encourages both producer and individual customers. Machine learning methods such as Naïve Bayes, maximum entropy classification, Deep Neural Networks were used upon the data, extracted from some websites such as Samsung and Apple for sentiment classification. In the online business arena, the application of sentiment classification explores a great opportunity. The subsidy of such an investigation is that associations can apply the proposed social examination framework to exploit the entire social information on the web and therefore improve their proper blueprint promoting strategies corresponding business.
16
Daradkeh, Mohammad. "Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media." International Journal of Business Analytics 9, no. 3 (July 2022): 1–22. http://dx.doi.org/10.4018/ijban.292056.
Full textAbstract:
This study presents a data analytics framework that aims to analyze topics and sentiments associated with COVID-19 vaccine misinformation in social media. A total of 40,359 tweets related to COVID-19 vaccination were collected between January 2021 and March 2021. Misinformation was detected using multiple predictive machine learning models. Latent Dirichlet Allocation (LDA) topic model was used to identify dominant topics in COVID-19 vaccine misinformation. Sentiment orientation of misinformation was analyzed using a lexicon-based approach. An independent-samples t-test was performed to compare the number of replies, retweets, and likes of misinformation with different sentiment orientations. Based on the data sample, the results show that COVID-19 vaccine misinformation included 21 major topics. Across all misinformation topics, the average number of replies, retweets, and likes of tweets with negative sentiment was 2.26, 2.68, and 3.29 times higher, respectively, than those with positive sentiment.
17
Efuwape, Temitope O., Temitope E. Abioye, and Adebisi K-K. Abdullah. "TEXT ANALYTICS OF OPINION-POLL ON ADOPTION OF DIGITAL COLLABORATIVE TOOLS FOR ACADEMIC PLANNING USING VADER-BASED LEXICON SENTIMENT ANALYSIS." FUDMA JOURNAL OF SCIENCES 6, no. 1 (March 31, 2022): 152–59. http://dx.doi.org/10.33003/fjs-2022-0601-874.
Full textAbstract:
The fast growing community of digital collaborative users across the globe continued to witness breaking of new frontiers in hitherto industries where deployment of traditional methods of computing had continued to hold sway. Notwithstanding the widespread deployment computing tools in educational institutions in Nigeria, the use of online collaborative tools is limited and seldom a commonplace in tertiary educational institutions for academic planning. This study therefore aims at extracting emotions from opinions expressed by stakeholders in the academic research industry regarding the utilitarian possibilities of collaborative tools for academic planning purposes through text mining. A VADER-based approach to Sentiment Analysis is modeled in the opinion mining study of the natural language processing use case. Assigning negative, positive, neutral and compound values to the uni-gram and bi-gram tokenized dictionary-of-known-words, experimental result shows a -0.10 mean sentiment negative score constituting a 17.27% clusters of respondents not favorably disposed to the idea while a 22.7% cluster of highly convinced respondents expressed positive sentiments about the use of collaborative tools with a mean sentiment score of 0.49. A 60.01% cluster of average respondents who expressed neutral sentiments actually tilts towards a positive emotion with a 0.39 mean score.
18
Daradkeh, Mohammad Kamel. "A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction." Electronics 11, no. 2 (January 13, 2022): 250. http://dx.doi.org/10.3390/electronics11020250.
Full textAbstract:
Stock market analysis plays an indispensable role in gaining knowledge about the stock market, developing trading strategies, and determining the intrinsic value of stocks. Nevertheless, predicting stock trends remains extremely difficult due to a variety of influencing factors, volatile market news, and sentiments. In this study, we present a hybrid data analytics framework that integrates convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) to evaluate the impact of convergence of news events and sentiment trends with quantitative financial data on predicting stock trends. We evaluated the proposed framework using two case studies from the real estate and communications sectors based on data collected from the Dubai Financial Market (DFM) between 1 January 2020 and 1 December 2021. The results show that combining news events and sentiment trends with quantitative financial data improves the accuracy of predicting stock trends. Compared to benchmarked machine learning models, CNN-BiLSTM offers an improvement of 11.6% in real estate and 25.6% in communications when news events and sentiment trends are combined. This study provides several theoretical and practical implications for further research on contextual factors that influence the prediction and analysis of stock trends.
19
Sholehurrohman, Ridho, and Igit Sabda Ilman. "ANALISIS SENTIMEN TWEET KASUS KEBOCORAN DATA PENGGUNAAN FACEBOOK OLEH CAMBRIGDE ANALYTICA." Jurnal Pepadun 3, no. 1 (April 1, 2022): 140–47. http://dx.doi.org/10.23960/pepadun.v3i1.108.
Full textAbstract:
The case of the Facebook user data leak by Cambridge Analytica has been spotlight in the public lately. Many of the citizens has participated discussing this case, especially in social media Twitter. Sentiment analysis is a computational research of opinions and emotions sentiment that are expressed textually. This study aims to classify positive and negative sentiment from Twitter data and to determine the accuracy of the classification model using Naïve Bayes Classifier method. Based on experiment conducted by tweet data with the “Zuckerberg” and “Cambridge Analytics” keywords, it has been produced Naïve Bayes Classifier with an accuracy of 83.06%.
20
Omar, Mohd Faizal, Nurul Husna Mahathir, Mohd Nasrun Mohd Nawi, and Faisal Zulhumadi. "Prototype Development and Pre-Commercialization Strategies for Mobile Based Property Analytics." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 10 (September 25, 2019): 198. http://dx.doi.org/10.3991/ijim.v13i10.11309.
Full textAbstract:
No wadays, the government agencies are looking for strategies to strengthen their newly implemented policies for the nation building. The grassroots or the customer’s sentiments is very important to develop an inclusive policy with a mixed of bottom-up approach to incorporates the customer’s opinion. However, due to the unique political landscape and multiracial in Malaysia, current commercial off-the-shelf Social Analytics are irrelevant to capture the sentiments of multilingual characteristic for Malaysian native speakers. Current Social Analytic tool are lacking the quality of analysis for foreign languages such as Malay which limits the businesses to localize advertisement for a specific geographical area. Hence, this research is proposed to develop a real-time social media analytics tool with sentiment analysis specifically in Malaysian context in order to engage and analyze the customer reviews and opinions. The main purpose of this paper is to demonstrate our approach to utilize data from social media platforms such as Facebook and Twitter in gaining valuable insights to drive and improve marketing strategy in property industry. This research developed a tool namely Property Analytics to assess public sentiments on specific property project or services. The methodology and approach to enhance from lab scale to pre-commercialization activities are outlined in this paper. It is anticipates that our work is relevant to real world application, improve stakeholder’s decision making and people’s quality of life.
21
Yadav, Madan Lal, Anurag Dugar, and Kuldeep Baishya. "Decoding Customer Opinion for Products or Brands Using Social Media Analytics." International Journal of Intelligent Information Technologies 18, no. 2 (April 2022): 1–20. http://dx.doi.org/10.4018/ijiit.296271.
Full textAbstract:
This study uses aspect level sentiment analysis using lexicon-based approach to analyse online reviews of an Indian brand called Patanjali, which sells many FMCG products under its name. These reviews have been collected from the microblogging site twitter from where a total of 4961 tweets about ten Patanjali branded products have been extracted and analysed. Along with the aspect level sentiment analysis, an opinion tagged corpora has also been developed. Machine learning approaches - Support Vector Machine (SVM), Decision Tree, and Naïve Bayes have also been used to perform the sentiment analysis and to figure out the appropriate classifiers suitable for such product reviews analysis. Authors first identify customer preferences and / or opinions about a product or brand by analyisng online customer reviews as they express them on social media platform, twitter by using aspect level sentiment analysis. Authors also address the limitations of scarcity of opinion tagged data, required to train supervised classifiers to perform sentiment analysis by developing tagged corpora.
22
Elsayed, Salma. "Predictive Analytics for Stock Prices using Sentiment Analysis." International Journal of Computer Applications 183, no. 48 (January 18, 2022): 32–37. http://dx.doi.org/10.5120/ijca2022921888.
Full text
23
Chen, Jinyan, Susanne Becken, and Bela Stantic. "Lexicon based Chinese language sentiment analysis method." Computer Science and Information Systems 16, no. 2 (2019): 639–55. http://dx.doi.org/10.2298/csis181015013c.
Full textAbstract:
The growing number of social media users and vast volume of posts could provide valuable information about the sentiment toward different locations, services as well as people. Recent advances in Big Data analytics and natural language processing often means to automatically calculate sentiment in these posts. Sentiment analysis is challenging and computationally demanding task due to the volume of data, misspelling, emoticons as well as abbreviations. While significant work was directed toward the sentiment analysis of English text there is limited attention in literature toward the sentiment analytic of Chinese language. In this work we propose method to identify the sentiment in Chinese social media posts and to test our method we rely on posts sent by visitors of Great Barrier Reef by users of most popular Chinese social media platform Sina Weibo. We elaborate process of capturing of weibo posts, describe a creation of lexicon as well as develop and explain algorithm for sentiment calculation. In case study, related to sentiment toward the different GBR destinations, we demonstrate that the proposed method is effective in obtaining the information and is suitable to monitor visitors? opinion.
24
Bourequat, Wasim, and Hassan Mourad. "Sentiment Analysis Approach for Analyzing iPhone Release using Support Vector Machine." International Journal of Advances in Data and Information Systems 2, no. 1 (April 30, 2021): 36–44. http://dx.doi.org/10.25008/ijadis.v2i1.1216.
Full textAbstract:
Sentiment analysis is a process of understanding, extracting, and processing textual data automatically to get sentiment information contained in a comment sentence on Twitter. Sentiment analysis needs to be done because the use of social media in society is increasing so that it affects the development of public opinion. Therefore, it can be used to analyze public opinion by applying data science, one of which is Natural Language Processing (NLP) and Text Mining or also known as text analytics. The stages of the overall method used in this study are to do text mining on the Twitter site regarding iPhone Release with methods of scraping, labeling, preprocessing (case folding, tokenization, filtering), TF-IDF, and classification of sentiments using the Support Vector Machine. The Support Vector Machine is widely used as a baseline in text-related tasks with satisfactory results, on several evaluation matrices such as accuracy, precision, recall, and F1 score yielding 89.21%, 92.43%, 95.53%, and 93.95, respectively.
25
Deepanshi and Adwitiya Sinha. "Self-Aware Contextual Behavior Analysis for Service Quality Assurance Over Social Networks." Journal of Cases on Information Technology 24, no. 3 (July 2022): 1–23. http://dx.doi.org/10.4018/jcit.20220701.oa8.
Full textAbstract:
Social media allows people to share their ideologue through an efficient channel of communication. The social dialogues carry sentiment in expression regarding a particular social profile, trend, or topic. In our research, we have collected real-time user comments and feedbacks from Twitter portals of two food delivery services. This is followed by the extraction of the most prevalent contexts using natural language analytics. Further, our proposed algorithmic framework is used to generate a signed social network to analyze the product-centric behavioral sentiment. Analysis of sentiment with the fine-grained level about contexts gave a broader view to evaluate and perform contextual predictions. Customer behavior is analyzed, and the outcome is received in terms of positive and negative contexts. The results from our social behavioral model predicted the positive and negative contextual sentiments of customers, which can be further used to help in deciding future strategies and assuring service quality for better customer satisfaction.
26
Haywood, M. Elizabeth, and Anubha Mishra. "Building a culture of business analytics: a marketing analytics exercise." International Journal of Educational Management 33, no. 1 (January 7, 2019): 86–97. http://dx.doi.org/10.1108/ijem-03-2018-0107.
Full textAbstract:
Purpose The purpose of this paper is to describe how brief exercises in introductory and advanced marketing courses can help business students achieve a broader understanding of what Big data and data analytics mean in the workplace. These short analytics problems fit into the culture that we are building at our institution to create analytics cases for courses within our business curriculum. Design/methodology/approach A database of 1,500 customer reviews for a fictitious sporting company was created. Two exercises based on text mining and sentiment analysis were developed to be tested in introductory and advanced marketing course. Students were introduced to the basic concepts used in data analysis and the creation of R code for extracting sentiment words was demonstrated. Students then used pivot tables to identify patterns in the given data set. Students in the introductory course completed a short exercise while the students in the advanced class developed a detailed memo. Findings Results suggest that students in the introductory course are significantly more aware of the use of data in the industry as well as methods to deal with Big data after completing the exercise as compared to their knowledge at the beginning of the exercise. Students in the advanced course are able to identify patterns, detect shortcoming and propose strategic plans based on their analysis of the data. Originality/value Proposed exercises in the study are developed with an aim to help business schools develop a culture supportive of analytics. The purpose of these exercises is to make students aware of the importance of Big data and analytics early on in their curriculum and reinforce their exposure in an advanced course.
27
Bhuvaneshwari, K., Dr S. A. Jyothi Rani, and Dr V. V. Haragopal. "Sentiment Analysis of Tweets on Telangana State Government Flagship Schemes." International Journal of Engineering and Advanced Technology 12, no. 1 (October 30, 2022): 23–27. http://dx.doi.org/10.35940/ijeat.a3794.1012122.
Full textAbstract:
Over the last decade, the usage of social media has evolved to a greater extent. Today, social media platforms like Twitter, facebook, snapchat are vastly used to incept the opinions of public about a particular entity. Social media has become a great source of text data. Text analytics plays a crucial role on social media data to give answers to a wide variety of questions about public feedback on many issues or topics. The primary objective of this work is to analyse the public opinion or sentiment in social media on Telangana state government welfare schemes. The purpose of sentiment analysis is to find opinions from tweets and extract sentiments from them and find their polarity, i.e., positive, neutral or negative. Here we are using twitter as it has gained much popularity and media attention. The first step is to extract the tweets on particular schemes through Twitter API and Python language followed by cleaning and pre- processing steps of the raw tweets. Then tfidf vectorizer was invoked for feature extraction and creation of bag of words and finally sentiment polarity scores were obtained by using VADER (Valence Aware Dictionary and sentiment Reasoner), lexicon and rule-based sentiment analysis tool.
28
Dwianto, Rahmad Agus, Achmad Nurmandi, and Salahudin Salahudin. "The Sentiments Analysis of Donald Trump and Jokowi’s Twitters on Covid-19 Policy Dissemination." Webology 18, no. 1 (April 29, 2021): 389–405. http://dx.doi.org/10.14704/web/v18i1/web18096.
Full textAbstract:
As Covid-19 spreads to other nations and governments attempt to minimize its effect by introducing countermeasures, individuals have often used social media outlets to share their opinions on the measures themselves, the leaders implementing them, and the ways in which their lives are shifting. Sentiment analysis refers to the application in source materials of natural language processing, computational linguistics, and text analytics to identify and classify subjective opinions. The reason why this research uses a sentiment case study towards Trump and Jokowi's policies is because Jokowi and Trump have similarities in handling Covid-19. Indonesia and the US are still low in the discipline in implementing health protocols. The data collection period was chosen on September 21 - October 21 2020 because during that period, the top 5 trending on Twitter included # covid19, #jokowi, #miglobal, #trump, and #donaldtrump. So, this period is most appropriate for taking data and discussing the handling of Covid-19 by Jokowi and Trump. The result shows both Jokowi and Trump have higher negative sentiments than positive sentiments during the period. Trump had issued a controversial statement regarding the handling of Covid-19. This research is limited to the sentiment generated by the policies conveyed by the US and Indonesian Governments via @jokowi and @realDonaldTrump Twitter Account. The dataset presented in this research is being collected and analyzed using the Brand24, a software-automated sentiment analysis. Further research can increase the scope of the data and increase the timeframe for data collection and develop tools for analyzing sentiment.
29
Alonso, Miguel A., David Vilares, Carlos Gómez-Rodríguez, and Jesús Vilares. "Sentiment Analysis for Fake News Detection." Electronics 10, no. 11 (June 5, 2021): 1348. http://dx.doi.org/10.3390/electronics10111348.
Full textAbstract:
In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.
30
Mane, Deepak, Dr Sirbi Kotrappa, and Kiran Shibe. "Sentiment Analytics on Chinese Product Boycott from Multiple Data Sources." Computational Intelligence and Machine Learning 2, no. 1 (April 20, 2021): 16–25. http://dx.doi.org/10.36647/ciml/02.01.a003.
Full textAbstract:
Sentiment Analysis and Opinion mining is a technique recognizing and drawing out the personalized information underlying a different kind of documents such as text, audio, images and videos. This area of research tries to exaplain the feeling, opinions, emotions of people on something topics. The most relevant classifying a statement as ‘positive’ , ‘negative’ and ‘neutral’ from records/posts obtained from different source system such as Twitter, Facebook , Reddit etc. To predict the sentiment/result of recent Chinese Product Boycott campaign, This paper direct to operate on data received from 9 different sources. In the field of Trade and commerce where traders. Politians and Peoples need to catch public’s point of view, thinking and therefor evaluate people’s reaction about Chinese product. The reasoning behind performing this research is that, the prediction will also help to know what is reason behind this , Chinese product boycott analysis will have a major impact on relationship between India and China trade. Keyword : Sentiment, Chinese Product, Data Sources, Boycott
31
G, Divya Bharathi, Jagan A, and Pradeep Kumar V. "Toxic Sentiment Identification Using R Programming." International Journal of Engineering Technology and Management Sciences 4, no. 5 (September 28, 2020): 76–81. http://dx.doi.org/10.46647/ijetms.2020.v04i05.014.
Full textAbstract:
Text messaging has become a universal staple. WhatsApp is regularly becoming a news delivery channel as users rely on its broadcast messages to share both local and international news. Today we are not utilizing and operating it, but it is operating us which can confirm to be very unsafe for us. Most of the fake news spread rapidly by WhatsApp. So, there is requirement to examine WhatsApp chat by user’s sentiment or opinion. WhatsApp is such an application which is used widely for transferring media, text, files as well as audio calling. WhatsApp is progressively becoming a turning point in numerous sectors like healthcare, education and business. So, there is requirement to inspect WhatsApp chat by user’s sentiment or opinion. The advent of the internet had played a huge role in expanding the usage of text messaging to instant messaging on mobile devices. WhatsApp chat sentiment analysis to increase improved insights regarding their employees and strive to stay away from unanticipated conflicts due to various redundancies and insufficiency of business processes. Sentiment analysis is most popular branches of textual analytics which with the aid of information and natural language processing observe and categorize the unorganized written data into different sentiments. It is as well as acknowledged as opinion mining. Most of the false news increase rapidly by WhatsApp. Therefore, there is call for to observe and examine WhatsApp chat to find user’s sentiment or opinion. Firstly, chat from WhatsApp is selected and exported to a system which is an easy task and can be done either by phone or WhatsApp for the computer system. Following this, the processes are fairly simple and have been explained with all the coding details needed to analyze the texts. In this project, chat of WhatsApp has been used as database by using R, sentiments and emotions are being analyzed.
32
Basili, Roberto, Danilo Croce, and Giuseppe Castellucci. "Dynamic polarity lexicon acquisition for advanced Social Media analytics." International Journal of Engineering Business Management 9 (January 1, 2017): 184797901774491. http://dx.doi.org/10.1177/1847979017744916.
Full textAbstract:
Social media analytics tool aims at eliciting information and knowledge about individuals and communities, as this emerges from the dynamics of interpersonal communications in the social networks. Sentiment analysis (SA) is a core component of this process as it focuses onto the subjective levels of this knowledge, including the agreement/rejection, the perception, and the expectations by which individual users socially evolve in the network. Analyzing user sentiments thus corresponds to recognize subjective opinions and preferences in the texts they produce in social contexts, gather collective evidence across one or more communities, and trace some inferences about the underlying social phenomena. Automatic SA is a complex process, often enabled by hand-coded dictionaries, called polarity lexicons, that are intended to capture the a priori emotional aspects of words or multiword expressions. The development of such resources is an expensive, and, mainly, language and task-dependent process. Resulting polarity lexicons may be inadequate at fully covering Social Media phenomena, which are intended to capture global communities. In the area of SA over Social Media, this article presents an unsupervised and language independent method for inducing large-scale polarity lexicons from a specific but representative medium, that is, Twitter. The model is based on a novel use of Distributional Lexical Semantics methodologies as these are applied to Twitter. Given a set of heuristically annotated messages, the proposed methodology transfers the known sentiment information of subjective sentences to individual words. The resulting lexical resource is a large-scale polarity lexicon whose effectiveness is measured with respect to different SA tasks in English, Italian, and Arabic. Comparison of our method with different Distributional Lexical Semantics paradigms confirms the beneficial impact of our method in the design of very accurate SA systems in several natural languages.
33
Hoeber, Orland, Larena Hoeber, Maha El Meseery, Kenneth Odoh, and Radhika Gopi. "Visual Twitter Analytics (Vista)." Online Information Review 40, no. 1 (February 8, 2016): 25–41. http://dx.doi.org/10.1108/oir-02-2015-0067.
Full textAbstract:
Purpose – Due to the size and velocity at which user generated content is created on social media services such as Twitter, analysts are often limited by the need to pre-determine the specific topics and themes they wish to follow. Visual analytics software may be used to support the interactive discovery of emergent themes. The paper aims to discuss these issues. Design/methodology/approach – Tweets collected from the live Twitter stream matching a user’s query are stored in a database, and classified based on their sentiment. The temporally changing sentiment is visualized, along with sparklines showing the distribution of the top terms, hashtags, user mentions, and authors in each of the positive, neutral, and negative classes. Interactive tools are provided to support sub-querying and the examination of emergent themes. Findings – A case study of using Vista to analyze sport fan engagement within a mega-sport event (2013 Le Tour de France) is provided. The authors illustrate how emergent themes can be identified and isolated from the large collection of data, without the need to identify these a priori. Originality/value – Vista provides mechanisms that support the interactive exploration among Twitter data. By combining automatic data processing and machine learning methods with interactive visualization software, researchers are relieved of tedious data processing tasks, and can focus on the analysis of high-level features of the data. In particular, patterns of Twitter use can be identified, emergent themes can be isolated, and purposeful samples of the data can be selected by the researcher for further analysis.
34
Adinolfi, Paola, Ernesto D'Avanzo, Miltiadis D. Lytras, Isabel Novo-Corti, and Jose Picatoste. "Sentiment Analysis to Evaluate Teaching Performance." International Journal of Knowledge Society Research 7, no. 4 (October 2016): 86–107. http://dx.doi.org/10.4018/ijksr.2016100108.
Full textAbstract:
The aim of this work is to review a specific learning analytics method - sentiment analysis - in the field of Higher Education, showing how it is employed to monitor student satisfaction on different platforms, and to propose an architecture of Sentiment Analysis for Higher Education purposes, which trace and unify what emerges from the literature review. First, a literature review is carried out, which proves the widespread and increasing interest of the communities, of both scholars and practitioners, in the use of sentiment analysis in the field of Higher Education. The analysis, focused on three different e-learning domains, identifies weaknesses and gaps, and in particular the lack of a unifying approach which is able to deal with the different domains. Secondly, a prototype architecture – LADEL (Learning Analytics Dashboard for E-Learning) - is introduced, which is able to deal with the different e-learning domains. Some preliminary experiments are carried out, highlighting some limitations and open issues, as stimulus to continue the development of the platform.
35
Wang, Yibo, Mingming Wang, and Wei Xu. "A Sentiment-Enhanced Hybrid Recommender System for Movie Recommendation: A Big Data Analytics Framework." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/8263704.
Full textAbstract:
Movie recommendation in mobile environment is critically important for mobile users. It carries out comprehensive aggregation of user’s preferences, reviews, and emotions to help them find suitable movies conveniently. However, it requires both accuracy and timeliness. In this paper, a movie recommendation framework based on a hybrid recommendation model and sentiment analysis on Spark platform is proposed to improve the accuracy and timeliness of mobile movie recommender system. In the proposed approach, we first use a hybrid recommendation method to generate a preliminary recommendation list. Then sentiment analysis is employed to optimize the list. Finally, the hybrid recommender system with sentiment analysis is implemented on Spark platform. The hybrid recommendation model with sentiment analysis outperforms the traditional models in terms of various evaluation criteria. Our proposed method makes it convenient and fast for users to obtain useful movie suggestions.
36
Daradkeh, Mohammad. "Organizational Adoption of Sentiment Analytics in Social Media Networks." International Journal of Information Technologies and Systems Approach 15, no. 2 (July 1, 2022): 1–29. http://dx.doi.org/10.4018/ijitsa.307023.
Full textAbstract:
Enterprise adoption and application of sentiment analytics (SA) has recently attracted significant interest from both academia and industry, as it offers exciting opportunities to generate competitive intelligence on consumer attitudes and opinions. Yet, there is limited understanding of the factors underlying successful and widespread adoption of SA in enterprises. This study presents a systematic literature review (SLR) to analyze and summarize previous research on corporate adoption of SA in social media. The SLR examines the results of 83 studies and focuses on tasks, techniques, application domains, and factors that influence enterprise adoption of SA. The findings provide insights into (i) key factors influencing SA adoption, (ii) research trends and paradigms across disciplines, and (iii) potential areas for future research on enterprise adoption of SA. These findings recommend actionable future research agendas for scholars and inform practitioners' understanding of the decision-making processes involved in enterprise adoption of SA in social media.
37
Khomsah, Siti, Rima Dias Ramadhani, and Sena Wijayanto. "Big Data Analytics to Analyze Sentiment, Emotions, and Perceptions of Travelers (Case Study: Tourism Destination in Purwokerto Indonesia)." Jurnal E-Komtek (Elektro-Komputer-Teknik) 5, no. 2 (December 30, 2021): 284–97. http://dx.doi.org/10.37339/e-komtek.v5i2.791.
Full textAbstract:
Big data analytics can extract travelers' sentiment, emotions, and experiences from their internet opinions. This study analyzes sentiment, emotion, and traveler experiences at eight tourism destinations in Purwokerto Central Java, Indonesia. The methods are lexicon using NCR vocabulary(EmoLex) and word cloud analysis. The results show visitors generally have a positive sentiment. The five destinations with high positive sentiment are the Village (91%), Lokawisata Baturaden(81%), Baturaden Forest (79%), Limpa Kuwus (78%), and Taman Andang(.77%). In comparison, other destinations achieve positive sentiment under 70%. Only a few visitors give negative sentiment to all tourism destinations. The emotion of visitors stands out in Joy and Trust. NRC revealed sadness dan anger emotion but only about 20%. Cloud analysis does not reveal a distinguish keyword because the word feature still contained noise such as conjunction, adverb, and the name of the sites. Further research must consider other text preprocessing to handle noises.
38
Jindal, Srishty, and Kamlesh Sharma. "A Review on Sentiment Classification: Natural Language Understanding." Recent Patents on Engineering 13, no. 1 (February 8, 2019): 20–27. http://dx.doi.org/10.2174/1872212112666180731113353.
Full textAbstract:
Background: With the tremendous increase in the use of social networking sites for sharing the emotions, views, preferences etc. a huge volume of data and text is available on the internet, there comes the need for understanding the text and analysing the data to determine the exact intent behind the same for a greater good. This process of understanding the text and data involves loads of analytical methods, several phases and multiple techniques. Efficient use of these techniques is important for an effective and relevant understanding of the text/data. This analysis can in turn be very helpful in ecommerce for targeting audience, social media monitoring for anticipating the foul elements from society and take proactive actions to avoid unethical and illegal activities, business analytics, market positioning etc. Method: The goal is to understand the basic steps involved in analysing the text data which can be helpful in determining sentiments behind them. This review provides detailed description of steps involved in sentiment analysis with the recent research done. Patents related to sentiment analysis and classification are reviewed to throw some light in the work done related to the field. Results: Sentiment analysis determines the polarity behind the text data/review. This analysis helps in increasing the business revenue, e-health, or determining the behaviour of a person. Conclusion: This study helps in understanding the basic steps involved in natural language understanding. At each step there are multiple techniques that can be applied on data. Different classifiers provide variable accuracy depending upon the data set and classification technique used.
39
Qiao, Fang, and Jago Williams. "Topic Modelling and Sentiment Analysis of Global Warming Tweets." Journal of Organizational and End User Computing 34, no. 3 (May 2022): 1–18. http://dx.doi.org/10.4018/joeuc.294901.
Full textAbstract:
With the increasing extreme weather events and various disasters, people are paying more attention to environmental issues than ever, particularly global warming. Public debate on it has grown on various platforms, including newspapers and social media. This paper examines the topics and sentiments of the discussion of global warming on Twitter over a span of 18 months using two big data analytics techniques—topic modelling and sentiment analysis. There are seven main topics concerning global warming frequently debated on Twitter: factors causing global warming, consequences of global warming, actions necessary to stop global warming, relations between global warming and Covid-19; global warming’s relation with politics, global warming as a hoax, and global warming as a reality. The sentiment analysis shows that most people express positive emotions about global warming, though the most evoked emotion found across the data is fear, followed by trust. The study provides a general and critical view of the public’s principal concerns and their feelings about global warming on Twitter.
40
Park, Seunghyun Brian, Jichul Jang, and Chihyung Michael Ok. "Analyzing Twitter to explore perceptions of Asian restaurants." Journal of Hospitality and Tourism Technology 7, no. 4 (November 14, 2016): 405–22. http://dx.doi.org/10.1108/jhtt-08-2016-0042.
Full textAbstract:
Purpose The purpose of this paper is to use Twitter analysis to explore diner perceptions of four types of Asian restaurants (Chinese, Japanese, Korean and Thai). Design/methodology/approach Using 86,015 tweets referring to Asian restaurants, this research used text mining and sentiment analysis to find meaningful patterns, popular words and emotional states in opinions. Findings Twitter users held mingled perceptions of different types of Asian restaurants. Sentiment analysis and ANOVA showed that the average sentiment scores for Chinese restaurants was significantly lower than the other three Asian restaurants. While most positive tweets referred to food quality, many negative tweets suggested problems associated with service quality or food culture. Research limitations/implications This research provides a methodology that future researchers can use in applying social media analytics to explore major issues and extract sentiment information from text messages. Originality/value Limited research has been conducted applying social media analysis in hospitality research. This study fills a gap by using social media analytics with Twitter data to examine the Twitter users’ thoughts and emotions for four different types of Asian restaurants.
41
Chun, Heuiju, Byung-Hak Leem, and Hyesun Suh. "Using text analytics to measure an effect of topics and sentiments on social-media engagement: Focusing on Facebook fan page of Toyota." International Journal of Engineering Business Management 13 (January 1, 2021): 184797902110162. http://dx.doi.org/10.1177/18479790211016268.
Full textAbstract:
In this study we investigate whether Facebook fan-page posting types and topics have a significant effect on engagement. More specifically, the media type and content theme of posting on Facebook are examined to see whether or not there was a difference between content topics. In order to achieve this goal, we set hypotheses as follows: (1) the media types of posting have a significant effect on engagement; (2) the topics and sentiment polarity of posting have a significant effect on engagement. We tested these hypotheses using research procedures as follows: (1) collection and preprocessing of social-media data, including posting types, comments, and reactions on Facebook fan pages, (2) topic modeling of fan-page postings using R and SAS, (3) testing hypotheses using a negative binomial regression model, and (4) implications and insights for social-media marketing. Topic modeling applying to textual data and sentiment analysis were conducted. After that, in order to find the factors to affect the number of Facebook fan-page engagements, the negative binomial regression model including post type, topic, sentiment, reactions of “love,” “haha,” and their interaction as exploratory variables was considered. Finally, the results show that post type is the most influential factor to affect social-media engagement, and content topics, sentiments of posts and comments also have significant effects on it.
42
Zhou, Fuli, Ming K. Lim, Yandong He, and Saurabh Pratap. "What attracts vehicle consumers’ buying." Industrial Management & Data Systems 120, no. 1 (November 20, 2019): 57–78. http://dx.doi.org/10.1108/imds-01-2019-0034.
Full textAbstract:
Purpose The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.
43
Singh, Shiwangi, Akshay Chauhan, and Sanjay Dhir. "Analyzing the startup ecosystem of India: a Twitter analytics perspective." Journal of Advances in Management Research 17, no. 2 (November 18, 2019): 262–81. http://dx.doi.org/10.1108/jamr-08-2019-0164.
Full textAbstract:
Purpose The purpose of this paper is to use Twitter analytics for analyzing the startup ecosystem of India. Design/methodology/approach The paper uses descriptive analysis and content analytics techniques of social media analytics to examine 53,115 tweets from 15 Indian startups across different industries. The study also employs techniques such as Naïve Bayes Algorithm for sentiment analysis and Latent Dirichlet allocation algorithm for topic modeling of Twitter feeds to generate insights for the startup ecosystem in India. Findings The Indian startup ecosystem is inclined toward digital technologies, concerned with people, planet and profit, with resource availability and information as the key to success. The study categorizes the emotions of tweets as positive, neutral and negative. It was found that the Indian startup ecosystem has more positive sentiments than negative sentiments. Topic modeling enables the categorization of the identified keywords into clusters. Also, the study concludes on the note that the future of the Indian startup ecosystem is Digital India. Research limitations/implications The analysis provides a methodology that future researchers can use to extract relevant information from Twitter to investigate any issue. Originality/value Any attempt to analyze the startup ecosystem of India through social media analysis is limited. This research aims to bridge such a gap and tries to analyze the startup ecosystem of India from the lens of social media platforms like Twitter.
44
Samuel, Jim, G. G. Md Nawaz Ali, Md Mokhlesur Rahman, Ek Esawi, and Yana Samuel. "COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification." Information 11, no. 6 (June 11, 2020): 314. http://dx.doi.org/10.3390/info11060314.
Full textAbstract:
Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.
45
Gupta, Abhishek, Dwijendra Nath Dwivedi, Jigar Shah, and Ravi Saroj. "Understanding Consumer Product Sentiments through Supervised Models on Cloud: Pre and Post COVID." Webology 18, no. 1 (April 29, 2021): 406–15. http://dx.doi.org/10.14704/web/v18i1/web18097.
Full textAbstract:
While a lot of work is done on extracting sentiments and opinions in unstructured text, majority of it is focused on contextual sentiment mining and features that are more focused on sentiments. The team attempted to use contextual text analytics to identify product or service features that drives the sentiment of the user. This is done through application of cosine similarity and neural networks. Customers speak about product or service feature when it is important for the them. The second stage of the analysis is focused on supervised learning, that identifies key drivers of a product or service. It helps in deriving those elements which are subconsciously being evaluated by customers but not spoken. We also test the significant difference in views of people pre and post Covid in their reviews. We found that factors related to Covid have gone up by 30% but not statistically significant. Given the volume of data, the team has analyzed these on cloud to assess the cloud computing readiness for such analysis. Feedback around the post Covid topics helps us understand the issues that need to be addressed by restaurant industry.
46
Sharma, Suyash, Mansha Kalra, and Ashu Sharma. "Amazon customer service: Big data analytics." Model Assisted Statistics and Applications 17, no. 4 (December 5, 2022): 231–37. http://dx.doi.org/10.3233/mas-220403.
Full textAbstract:
“Amazon Big Data”, conducts a thorough analysis on the e-commerce industry using big data and how certain trends can affect the functioning of the organizations delving in the field. With the growth of e-commerce, there has been a significant rise of the online consumers’ footprint. Companies such as Amazon, Flipkart and other e-commercial platforms have accrued huge chunks of consumer information, especially since the start of the pandemic. In this industry, reviews and ratings given to a product play a crucial role in determining the sentiments of the customers associated towards making the final purchase. Such factors account for the brand’s sales and image. In today’s landscape, a careful customer goes through the ratings of the product, its reviews which serve as a medium of screening. In a tie between two similar products, customers purchase a product with higher ratings and better reviews. Therefore, this leads us to the development of an ideal rating metric that is significant for the sales of the product. Moreover, become a tool for product differentiation. This manuscript is a method to standardize the ratings of customers and preserve the sanctity of the data. We discuss models which are an amalgamation of customer ratings, their respective reviews and a sentiment scored derived from the same review. These models also help us define customer clusters with different personalities based on their reviews and ratings. In addition to this, customer segmentation is a future scope to deep dive into the sales data and understand the financial behavior of a customer.
47
Gallagher, Conor, Eoghan Furey, and Kevin Curran. "The Application of Sentiment Analysis and Text Analytics to Customer Experience Reviews to Understand What Customers Are Really Saying." International Journal of Data Warehousing and Mining 15, no. 4 (October 2019): 21–47. http://dx.doi.org/10.4018/ijdwm.2019100102.
Full textAbstract:
In a world of ever-growing customer data, businesses are required to have a clear line of sight into what their customers think about the business, its products, people and how it treats them. Insight into these critical areas for a business will aid in the development of a robust customer experience strategy and in turn drive loyalty and recommendations to others by their customers. It is key for business to access and mine their customer data to drive a modern customer experience. This article investigates the use of a text mining approach to aid sentiment analysis in the pursuit of understanding what customers are saying about products, services and interactions with a business. This is commonly known as Voice of the Customer (VOC) data and it is key to unlocking customer sentiment. The authors analyse the relationship between unstructured customer sentiment in the form of verbatim feedback and structured data in the form of user review ratings or satisfaction ratings to explore the question of whether customers say what they really think when given the opportunity to provide free text feedback as opposed to how they rate a product on a scale of one to five. Using various Sentiment Analysis approaches, the authors assign a sentiment score to a piece of verbatim feedback and then categorise it as positive, negative, or neutral. Using this normalised sentiment score, they compare it to the corresponding rating score and investigate the potential business insights. The results obtained indicate that a business cannot rely solely on a standalone single metric as a source of truth regarding customer experience. There is a significant difference between the customer ratings score and the sentiment of their corresponding review of the product. The authors propose that it is imperative that a business supplements their customer feedback scores with a robust sentiment analysis strategy.
48
Palit, Sandip, and Soumadip Ghosh. "Real Time Sentiment Analysis." International Journal of Synthetic Emotions 11, no. 1 (January 2020): 27–35. http://dx.doi.org/10.4018/ijse.2020010103.
Full textAbstract:
Data is the most valuable resource. We have a lot of unstructured data generated by the social media giants Twitter, Facebook, and Google. Unfortunately, analytics on unstructured data cannot be performed. As the availability of the internet became easier, people started using social media platforms as the primary medium for sharing their opinions. Every day, millions of opinions from different parts of the world are posted on Twitter. The primary goal of Twitter is to let people share their opinion with a big audience. So, if the authors can effectively analyse the tweets, valuable information can be gained. Storing these opinions in a structured manner and then using that to analyse people's reactions and perceptions about buying a product or a service is a very vital step for any corporate firm. Sentiment analysis aims to analyse and discover the sentiments behind opinions of various people on different subjects like commercial products, politics, and daily societal issues. This research has developed a model to determine the polarity of a keyword in real time.
49
Cheng, Otto K. M., and Raymond Lau. "Big Data Stream Analytics for Near Real-Time Sentiment Analysis." Journal of Computer and Communications 03, no. 05 (2015): 189–95. http://dx.doi.org/10.4236/jcc.2015.35024.
Full text
50
Goshima, Keiichi, and Hiroshi Takahashi. "Building a Sentiment Dictionary for News Analytics using Stock Prices." Journal of Natural Language Processing 24, no. 4 (2017): 547–77. http://dx.doi.org/10.5715/jnlp.24.547.
Full text