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1
Tian, Jing, Wushour Slamu, Miaomiao Xu, Chunbo Xu, and Xue Wang. "Research on Aspect-Level Sentiment Analysis Based on Text Comments." Symmetry 14, no. 5 (May 23, 2022): 1072. http://dx.doi.org/10.3390/sym14051072.
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Sentiment analysis is the processing of textual data and giving positive or negative opinions to sentences. In the ABSA dataset, most sentences contain one aspect of sentiment polarity, or sentences of one aspect have multiple identical sentiment polarities, which weakens the sentiment polarity of the ABSA dataset. Therefore, this paper uses the SemEval 14 Restaurant Review dataset, in which each document is symmetrically divided into individual sentences, and two versions of the datasets ATSA and ACSA are created. ATSA: Aspect Term Sentiment Analysis Dataset. ACSA: Aspect Category Sentiment Analysis Dataset. In order to symmetrically simulate the complex relationship between aspect contexts and accurately extract the polarity of emotional features, this paper combines the latest development trend of NLP, combines capsule network and BRET, and proposes the baseline model CapsNet-BERT. The experimental results verify the effectiveness of the model.
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Abdullah Haje, Umran, Mohammed Hussein Abdalla, Reben Mohammed Saleem Kurda, and Zhwan Mohammed Khalid. "A New Model for Emotions Analysis in Social Network Text Using Ensemble Learning and Deep learning." Academic Journal of Nawroz University 11, no. 1 (March 9, 2022): 130–40. http://dx.doi.org/10.25007/ajnu.v11n1a1250.
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Recently, emotion analysis has become widely used. Therefore, increasing the accuracy of existing methods has become a challenge for researchers. The proposed method in this paper is a hybrid model to improve the accuracy of emotion analysis; Which uses a combination of convolutional neural network and ensemble learning. In the proposed method, after receiving the dataset, the data is pre-processed and converted into process able samples. Then the new dataset is split into two categories of training and test. The proposed model is a structure for machine learning in the form of ensemble learning. It contains blocks consisting of a combination of convolutional networks and basic classification algorithms. In each convolutional network, the base classification algorithms replace the fully connected layer. Evaluate the proposed method, in IMDB, PL04 and SemEval dataset with accuracy, precision, recall and F1 criteria, shows that, on average, for all three datasets, the precision of polarity detection is 90%, the recall of polarity detection is 93%, the F1 of polarity detection is 91% and finally the accuracy of polarity detection is 92%.
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Al-Kabi, Mohammed N., Heider A. Wahsheh, and Izzat M. Alsmadi. "Polarity Classification of Arabic Sentiments." International Journal of Information Technology and Web Engineering 11, no. 3 (July 2016): 32–49. http://dx.doi.org/10.4018/ijitwe.2016070103.
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Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.
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Jung, Soon-Gyo, Joni Salminen, and Bernard J. Jansen. "Engineers, Aware! Commercial Tools Disagree on Social Media Sentiment: Analyzing the Sentiment Bias of Four Major Tools." Proceedings of the ACM on Human-Computer Interaction 6, EICS (June 14, 2022): 1–20. http://dx.doi.org/10.1145/3532203.
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Large commercial sentiment analysis tools are often deployed in software engineering due to their ease of use. However, it is not known how accurate these tools are, and whether the sentiment ratings given by one tool agree with those given by another tool. We use two datasets - (1) NEWS consisting of 5,880 news stories and 60K comments from four social media platforms: Twitter, Instagram, YouTube, and Facebook; and (2) IMDB consisting of 7,500 positive and 7,500 negative movie reviews - to investigate the agreement and bias of four widely used sentiment analysis (SA) tools: Microsoft Azure (MS), IBM Watson, Google Cloud, and Amazon Web Services (AWS). We find that the four tools assign the same sentiment on less than half (48.1%) of the analyzed content. We also find that AWS exhibits neutrality bias in both datasets, Google exhibits bi-polarity bias in the NEWS dataset but neutrality bias in the IMDB dataset, and IBM and MS exhibit no clear bias in the NEWS dataset but have bi-polarity bias in the IMDB dataset. Overall, IBM has the highest accuracy relative to the known ground truth in the IMDB dataset. Findings indicate that psycholinguistic features - especially affect, tone, and use of adjectives - explain why the tools disagree. Engineers are urged caution when implementing SA tools for applications, as the tool selection affects the obtained sentiment labels.
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Tripathy, Abinash, and Santanu Kumar Rath. "Classification of Sentiment of Reviews using Supervised Machine Learning Techniques." International Journal of Rough Sets and Data Analysis 4, no. 1 (January 2017): 56–74. http://dx.doi.org/10.4018/ijrsda.2017010104.
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Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.
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Kuriyozov, Elmurod, and Sanatbek Matlatipov. "Building a New Sentiment Analysis Dataset for Uzbek Language and Creating Baseline Models." Proceedings 21, no. 1 (August 2, 2019): 37. http://dx.doi.org/10.3390/proceedings2019021037.
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Making natural language processing technologies available for low-resource languages is an important goal to improve the access to technology in their communities of speakers. In this paper, we provide the first annotated corpora for polarity classification for Uzbek language. Our methodology considers collecting a medium-size manually annotated dataset and a larger-size dataset automatically translated from existing resources. Then, we use these datasets to train sentiment analysis models on the Uzbek language, using both traditional machine learning techniques and recent deep learning models.
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Pecar, Samuel, Tobias Daudert, and Marian Simko. "Evaluation of end-to-end aspect-based sentiment analysis methods employing novel benchmark dataset for aspect, and opinion review analysis." Intelligent Data Analysis 26, no. 6 (November 12, 2022): 1617–41. http://dx.doi.org/10.3233/ida-216252.
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Aspect-based sentiment analysis (ABSA) deals with the determination of sentiments for opinion targets. While historically this research task has been addressed with pipeline approaches, more recent works use neural networks to jointly deal with the aspect term and opinion term extraction, as well as the polarity classification. Although learned together, most NN-based approaches and all pipeline approaches do not model correlations between the tasks. This is also based on the absence of adequate datasets which are annotated for all sub-tasks in a unified tagging scheme. We address this bottleneck and introduce the first purposely designed and annotated dataset for ABSA. The DAORA dataset covers 2,100 Tripadvisor reviews, and it is annotated on aspect terms, opinion terms, as well as aspect term polarity, using a unified tagging scheme. It was designed especially for end-to-end aspect-based sentiment analysis of real-world reviews and does not use any sentence repetition or removal. We evaluate the DAORA dataset in several experiments employing state-of-the-art models for ABSA. We set benchmarks and analyze the strengths as well as weaknesses of the data and approaches.
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Kouadri, Wissam Mammar, Mourad Ouziri, Salima Benbernou, Karima Echihabi, Themis Palpanas, and Iheb Ben Amor. "Quality of sentiment analysis tools." Proceedings of the VLDB Endowment 14, no. 4 (December 2020): 668–81. http://dx.doi.org/10.14778/3436905.3436924.
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In this paper, we present a comprehensive study that evaluates six state-of-the-art sentiment analysis tools on five public datasets, based on the quality of predictive results in the presence of semantically equivalent documents, i.e., how consistent existing tools are in predicting the polarity of documents based on paraphrased text. We observe that sentiment analysis tools exhibit intra-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents by the same tool, and inter-tool inconsistency , which is the prediction of different polarity for semantically equivalent documents across different tools. We introduce a heuristic to assess the data quality of an augmented dataset and a new set of metrics to evaluate tool inconsistencies. Our results indicate that tool inconsistencies is still an open problem, and they point towards promising research directions and accuracy improvements that can be obtained if such inconsistencies are resolved.
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Alghazzawi, Daniyal M., Anser Ghazal Ali Alquraishee, Sahar K. Badri, and Syed Hamid Hasan. "ERF-XGB: Ensemble Random Forest-Based XG Boost for Accurate Prediction and Classification of E-Commerce Product Review." Sustainability 15, no. 9 (April 23, 2023): 7076. http://dx.doi.org/10.3390/su15097076.
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Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed through tokenization, lemmatization, and stemming operations. The Harris hawk optimization (HHO) algorithm selects two datasets’ corresponding features. Finally, the sentiments from online reviews are classified into positive and negative categories regarding the proposed ERF-XGB approach. Hyperparameter tuning is used to find the optimal parameter values that improve the performance of the proposed ERF-XGB algorithm. The performance of the proposed ERF-XGB approach is analyzed using evaluation indicators, namely accuracy, recall, precision, and F1-score, for different existing approaches. Compared with the existing method, the proposed ERF-XGB approach effectively predicts sentiments of online product reviews with an accuracy rate of about 98.7% for the ChnSentiCorp dataset and 98.2% for the IMDB dataset.
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Zhao, Runcong, Lin Gui, Hanqi Yan, and Yulan He. "Tracking Brand-Associated Polarity-Bearing Topics in User Reviews." Transactions of the Association for Computational Linguistics 11 (2023): 404–18. http://dx.doi.org/10.1162/tacl_a_00555.
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Abstract Monitoring online customer reviews is important for business organizations to measure customer satisfaction and better manage their reputations. In this paper, we propose a novel dynamic Brand-Topic Model (dBTM) which is able to automatically detect and track brand-associated sentiment scores and polarity-bearing topics from product reviews organized in temporally ordered time intervals. dBTM models the evolution of the latent brand polarity scores and the topic-word distributions over time by Gaussian state space models. It also incorporates a meta learning strategy to control the update of the topic-word distribution in each time interval in order to ensure smooth topic transitions and better brand score predictions. It has been evaluated on a dataset constructed from MakeupAlley reviews and a hotel review dataset. Experimental results show that dBTM outperforms a number of competitive baselines in brand ranking, achieving a good balance of topic coherence and uniqueness, and extracting well-separated polarity-bearing topics across time intervals.1
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Styles, Erin, Ji-Young Youn, Mojca Mattiazzi Usaj, and Brenda Andrews. "Functional genomics in the study of yeast cell polarity: moving in the right direction." Philosophical Transactions of the Royal Society B: Biological Sciences 368, no. 1629 (November 5, 2013): 20130118. http://dx.doi.org/10.1098/rstb.2013.0118.
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The budding yeast Saccharomyces cerevisiae has been used extensively for the study of cell polarity, owing to both its experimental tractability and the high conservation of cell polarity and other basic biological processes among eukaryotes. The budding yeast has also served as a pioneer model organism for virtually all genome-scale approaches, including functional genomics, which aims to define gene function and biological pathways systematically through the analysis of high-throughput experimental data. Here, we outline the contributions of functional genomics and high-throughput methodologies to the study of cell polarity in the budding yeast. We integrate data from published genetic screens that use a variety of functional genomics approaches to query different aspects of polarity. Our integrated dataset is enriched for polarity processes, as well as some processes that are not intrinsically linked to cell polarity, and may provide new areas for future study.
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Mohamed Mostafa, Ayman. "Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset." Intelligent Automation & Soft Computing 35, no. 1 (2023): 1015–34. http://dx.doi.org/10.32604/iasc.2023.028041.
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A. Al Shamsi, Arwa, and Sherief Abdallah. "Sentiment Analysis of Emirati Dialects." Big Data and Cognitive Computing 6, no. 2 (May 17, 2022): 57. http://dx.doi.org/10.3390/bdcc6020057.
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Recently, extensive studies and research in the Arabic Natural Language Processing (ANLP) field have been conducted for text classification and sentiment analysis. Moreover, the number of studies that target Arabic dialects has also increased. In this research paper, we constructed the first manually annotated dataset of the Emirati dialect for the Instagram platform. The constructed dataset consisted of more than 70,000 comments, mostly written in the Emirati dialect. We annotated the comments in the dataset based on text polarity, dividing them into positive, negative, and neutral categories, and the number of annotated comments was 70,000. Moreover, the dataset was also annotated for the dialect type, categorized into the Emirati dialect, Arabic dialects, and MSA. Preprocessing and TF-IDF features extraction approaches were applied to the constructed Emirati dataset to prepare the dataset for the sentiment analysis experiment and improve its classification performance. The sentiment analysis experiment was carried out on both balanced and unbalanced datasets using several machine learning classifiers. The evaluation metrics of the sentiment analysis experiments were accuracy, recall, precision, and f-measure. The results reported that the best accuracy result was 80.80%, and it was achieved when the ensemble model was applied for the sentiment classification of the unbalanced dataset.
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Pevtsov, Alexei A., Kseniya A. Tlatova, Alexander A. Pevtsov, Elina Heikkinen, Ilpo Virtanen, Nina V. Karachik, Luca Bertello, Andrey G. Tlatov, Roger Ulrich, and Kalevi Mursula. "Reconstructing solar magnetic fields from historical observations." Astronomy & Astrophysics 628 (August 2019): A103. http://dx.doi.org/10.1051/0004-6361/201834985.
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Context. Systematic observations of magnetic field strength and polarity in sunspots began at Mount Wilson Observatory (MWO), USA in early 1917. Except for a few brief interruptions, this historical dataset has continued until the present. Aims. Sunspot field strength and polarity observations are critical in our project of reconstructing the solar magnetic field over the last hundred years. We provide a detailed description of the newly digitized dataset of drawings of sunspot magnetic field observations. Methods. The digitization of MWO drawings is based on a software package that we developed. It includes a semiautomatic selection of solar limbs and other features of the drawing, and a manual entry of the time of observations, measured field strength, and other notes handwritten on each drawing. The data are preserved in an MySQL database. Results. We provide a brief history of the project and describe the results from digitizing this historical dataset. We also provide a summary of the final dataset and describe its known limitations. Finally, we compare the sunspot magnetic field measurements with those from other instruments, and demonstrate that, if needed, the dataset could be continued using modern observations such as, for example, the Vector Stokes Magnetograph on the Synoptic Optical Long-term Investigations of the Sun platform.
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Sarkar, Kamal. "Sentiment Polarity Detection in Bengali Tweets Using Deep Convolutional Neural Networks." Journal of Intelligent Systems 28, no. 3 (July 26, 2019): 377–86. http://dx.doi.org/10.1515/jisys-2017-0418.
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Abstract Sentiment polarity detection is one of the most popular sentiment analysis tasks. Sentiment polarity detection in tweets is a more difficult task than sentiment polarity detection in review documents, because tweets are relatively short and they contain limited contextual information. Although the amount of blog posts, tweets and comments in Indian languages is rapidly increasing on the web, research on sentiment analysis in Indian languages is at the early stage. In this paper, we present an approach that classifies the sentiment polarity of Bengali tweets using deep neural networks which consist of one convolutional layer, one hidden layer and one output layer, which is a soft-max layer. Our proposed approach has been tested on the Bengali tweet dataset released for Sentiment Analysis in Indian Languages contest 2015. We have compared the performance of our proposed convolutional neural networks (CNN)-based model with a sentiment polarity detection model that uses deep belief networks (DBN). Our experiments reveal that the performance of our proposed CNN-based system is better than our implemented DBN-based system and some existing Bengali sentiment polarity detection systems.
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Haralabopoulos, Giannis, Ioannis Anagnostopoulos, and Derek McAuley. "Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content." Algorithms 13, no. 4 (April 1, 2020): 83. http://dx.doi.org/10.3390/a13040083.
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Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5 % to 5.4 % .
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Ghabayen, Ayman S., and Basem H. Ahmed. "Polarity Analysis of Customer Reviews Based on Part-of-Speech Subcategory." Journal of Intelligent Systems 29, no. 1 (August 15, 2019): 1535–44. http://dx.doi.org/10.1515/jisys-2018-0356.
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Abstract Nowadays, sentiment analysis is a method used to analyze the sentiment of the feedback given by a user in an online document, such as a blog, comment, and review, and classifies it as negative, positive, or neutral. The classification process relies upon the analysis of the polarity features of the natural language text given by users. Polarity analysis has been an important subtask in sentiment analysis; however, detecting correct polarity has been a major issue. Different researchers have utilized different polarity features, such as standard part-of-speech (POS) tags such as adjectives, adverbs, verbs, and nouns. However, there seems to be a lack of research focusing on the subcategories of these tags. The aim of this research was to propose a method that better recognizes the polarity of natural language text by utilizing different polarity features using the standard POS category and the subcategory combinations in order to explore the specific polarity of text. Several experiments were conducted to examine and compare the efficacies of the proposed method in terms of F-measure, recall, and precision using an Amazon dataset. The results showed that JJ + NN + VB + RB + VBP + RP, which is a POS subcategory combination, obtained better accuracy compared to the baseline approaches by 4.4% in terms of F-measure.
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Aiyanyo, Imatitikua D., Hamman Samuel, and Heuiseok Lim. "Effects of the COVID-19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning." Sustainability 13, no. 9 (April 29, 2021): 4986. http://dx.doi.org/10.3390/su13094986.
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In this study, we qualitatively and quantitatively examine the effects of COVID-19 on classrooms, students, and educators. Using a new Twitter dataset specific to South Korea during the pandemic, we sample the sentiment and strain on students and educators using applied machine learning techniques in order to identify various topical pain points emerging during the pandemic. Our contributions include a novel and open source geo-fenced dataset on student and educator opinion within South Korea that we are making available to other researchers as well. We also identify trends in sentiment and polarity over the pandemic timeline, as well as key drivers behind the sentiments. Moreover, we provide a comparative analysis of two widely used pre-trained sentiment analysis approaches with TextBlob and VADER using statistical significance tests. Ultimately, we analyze how public opinion shifted on the pandemic in terms of positive sentiments about accessing course materials, online support communities, access to classes, and creativity, to negative sentiments about mental fatigue, job loss, student concerns, and overwhelmed institutions. We also initiate initial discussions about the concept of actionable sentiment analysis by overlapping polarity with the concept of trigger management to assist users in coping with negative emotions. We hope that insights from this preliminary study can promote further utilization of social media datasets to evaluate government messaging, population sentiment, and multi-dimensional analysis of pandemics.
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Lu, Shan, Jichang Zhao, and Huiwen Wang. "Trading Imbalance in Chinese Stock Market—A High-Frequency View." Entropy 22, no. 8 (August 15, 2020): 897. http://dx.doi.org/10.3390/e22080897.
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Although an imbalance of buying and selling profoundly affects the formation of market trends, a fine-granularity investigation of this perplexity of trading behavior is still missing. Instead of using existing entropy measures, this paper proposed a new indicator based on transaction dataset that enables us to inspect both the direction and the magnitude of this imbalance at high frequency, which we call “polarity”. The polarity aims to measure the unevenness of the very essence trading desire based on the most micro decision making units. We investigate the relationship between the polarity and the return at both market-level and stock-level and find that the autocorrelated polarities cause a positive relation between lagged polarities and returns, while the current polarity is the opposite. It is also revealed that these associations shift according to the market conditions. In fact, when aggregating the one-minute polarities into daily signals, we find not only significant correlations disclosed by the market polarity and market emotion, but also the reliability of these signals in terms of reflecting the transitions of market-level behavior. These results imply that our presented polarity can reflect the market sentiment and condition in real time. Indeed, the trading polarity provides a new indicator from a high-frequency perspective to understand and foresee the market’s behavior in a data-driven manner.
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Telegin, Felix Y., Viktoria S. Karpova, Anna O. Makshanova, Roman G. Astrakhantsev, and Yuriy S. Marfin. "Solvatochromic Sensitivity of BODIPY Probes: A New Tool for Selecting Fluorophores and Polarity Mapping." International Journal of Molecular Sciences 24, no. 2 (January 7, 2023): 1217. http://dx.doi.org/10.3390/ijms24021217.
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This research work is devoted to collecting a high-quality dataset of BODIPYs in a series of 10–30 solvents. In total, 115 individual compounds in 71 solvents are represented by 1698 arrays of the spectral and photophysical properties of the fluorophore. Each dye for a series of solvents is characterized by a calculated value of solvatochromic sensitivity according to a semiempirical approach applied to a series of solvents. The whole dataset is classified into 6 and 24 clusters of solvatochromic sensitivity, from high negative to high positive solvatochromism. The results of the analysis are visualized by the polarity mapping plots depicting, in terms of wavenumbers, the absorption versus emission, stokes shift versus − (absorption maxima + emission maxima), and quantum yield versus stokes shift. An analysis of the clusters combining several dyes in an individual series of solvents shows that dyes of a high solvatochromic sensitivity demonstrate regular behaviour of the corresponding plots suitable for polarity and viscosity mapping. The fluorophores collected in this study represent a high quality dataset of pattern dyes for analytical and bioanalytical applications. The developed tools could be applied for the analysis of the applicability domain of the fluorescent sensors.
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Liu, Xu, Abdelouahed Gherbi, Wubin Li, Zhenzhou Wei, and Mohamed Cheriet. "TaijiGNN: A New Cycle-Consistent Generative Neural Network for High-Quality Bidirectional Transformation between RGB and Multispectral Domains." Sensors 21, no. 16 (August 10, 2021): 5394. http://dx.doi.org/10.3390/s21165394.
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Since multispectral images (MSIs) and RGB images (RGBs) have significantly different definitions and severely imbalanced information entropies, the spectrum transformation between them, especially reconstructing MSIs from RGBs, is a big challenge. We propose a new approach, the Taiji Generative Neural Network (TaijiGNN), to address the above-mentioned problems. TaijiGNN consists of two generators, G_MSI, and G_RGB. These two generators establish two cycles by connecting one generator’s output with the other’s input. One cycle translates the RGBs into the MSIs and converts the MSIs back to the RGBs. The other cycle does the reverse. The cycles can turn the problem of comparing two different domain images into comparing the same domain images. In the same domain, there are neither different domain definition problems nor severely underconstrained challenges, such as reconstructing MSIs from RGBs. Moreover, according to several investigations and validations, we effectively designed a multilayer perceptron neural network (MLP) to substitute the convolutional neural network (CNN) when implementing the generators to make them simple and high performance. Furthermore, we cut off the two traditional CycleGAN’s identity losses to fit the spectral image translation. We also added two consistent losses of comparing paired images to improve the two generators’ training effectiveness. In addition, during the training process, similar to the ancient Chinese philosophy Taiji’s polarity Yang and polarity Yin, the two generators update their neural network parameters by interacting with and complementing each other until they all converge and the system reaches a dynamic balance. Furthermore, several qualitative and quantitative experiments were conducted on the two classical datasets, CAVE and ICVL, to evaluate the performance of our proposed approach. Promising results were obtained with a well-designed simplistic MLP requiring a minimal amount of training data. Specifically, in the CAVE dataset, to achieve comparable state-of-the-art results, we only need half of the dataset for training; for the ICVL dataset, we used only one-fifth of the dataset to train the model, but obtained state-of-the-art results.
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Gade, Prof Swati. "Product Fake Reviews Detection with Sentiment Analysis Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 5863–68. http://dx.doi.org/10.22214/ijraset.2023.53030.
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Abstract: Recently, Sentiment Analysis (SA) has become one of the most interesting topics in text analysis, due to its promising commercial benefits. One of the main issues facing SA is how to extract emotions inside the opinion, and how to detect fake positive reviews and fake negative reviews from opinion reviews. Moreover, the opinion reviews obtained from users can be classified into positive or negative reviews, which can be used by a consumer to select a product. This paper aims to classify product reviews into groups of positive or negative polarity by using machine learning algorithms. In this study, we analyse online product reviews using SA methods in order to detect fake reviews. SA and text classification methods are applied to a dataset of product reviews. More specifically, we compare five supervised machine learning algorithms: Support Vector Machine (SVM), for sentiment classification of reviews using two different datasets, including product review dataset V2.0 and product reviews dataset V1.0. The measured results of our experiments show that the SVM algorithm outperforms other algorithms, and that it reaches thehighest accuracy not only in text classification, but also in detecting fake reviews.
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Angelidis, Stefanos, and Mirella Lapata. "Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis." Transactions of the Association for Computational Linguistics 6 (December 2018): 17–31. http://dx.doi.org/10.1162/tacl_a_00002.
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We consider the task of fine-grained sentiment analysis from the perspective of multiple instance learning (MIL). Our neural model is trained on document sentiment labels, and learns to predict the sentiment of text segments, i.e. sentences or elementary discourse units (EDUs), without segment-level supervision. We introduce an attention-based polarity scoring method for identifying positive and negative text snippets and a new dataset which we call SpoT (as shorthand for Segment-level POlariTy annotations) for evaluating MIL-style sentiment models like ours. Experimental results demonstrate superior performance against multiple baselines, whereas a judgement elicitation study shows that EDU-level opinion extraction produces more informative summaries than sentence-based alternatives.
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Mostafa, Ayman Mohamed, Meeaad Aljasir, Meshrif Alruily, Ahmed Alsayat, and Mohamed Ezz. "Innovative Forward Fusion Feature Selection Algorithm for Sentiment Analysis Using Supervised Classification." Applied Sciences 13, no. 4 (February 5, 2023): 2074. http://dx.doi.org/10.3390/app13042074.
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Sentiment analysis is considered one of the significant trends of the recent few years. Due to the high importance and increasing use of social media and electronic services, the need for reviewing and enhancing the provided services has become crucial. Revising the user services is based mainly on sentiment analysis methodologies for analyzing users’ polarities to different products and applications. Sentiment analysis for Arabic reviews is a major concern due to high morphological linguistics and complex polarity terms expressed in the reviews. In addition, the users can present their orientation towards a service or a product by using a hybrid or mix of polarity terms related to slang and standard terminologies. This paper provides a comprehensive review of recent sentiment analysis methods based on lexicon or machine learning (ML). The comparison provides a clear vision of the number of classes, the used dialect, the annotated algorithms, and their performance. The proposed methodology is based on cross-validation of Arabic data using a k-fold mechanism that splits the dataset into training and testing folds; subsequently, the data preprocessing is executed to clean sentiments from unwanted terms that can affect data analysis. A vectorization of the dataset is then applied using TF–IDF for counting word and polarity terms. Furthermore, a feature selection stage is processed using Pearson, Chi2, and Random Forest (RF) methods for mapping the compatibility between input and target features. This paper also proposed an algorithm called the forward fusion feature for sentiment analysis (FFF-SA) to provide a feature selection that applied different machine learning (ML) classification models for each chunk of k features and accumulative features on the Arabic dataset. The experimental results measured and scored all accuracies between the feature importance method and ML models. The best accuracy is recorded with the Naïve Bayes (NB) model with the RF method.
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Singh, Purva. "Covhindia: Deep Learning Framework for Sentiment Polarity Detection of Covid-19 Tweets in Hindi." International Journal on Natural Language Computing 9, no. 5 (October 30, 2020): 23–34. http://dx.doi.org/10.5121/ijnlc.2020.9502.
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On 11th March 2020, the World Health Organization (WHO) declared Corona Virus Disease of 2019 (COVID-19) as a pandemic. Over time, the exponential growth of this disease has highlighted a mixture of sentiments expressed by the general population from various parts of the world speaking varied languages. It is, therefore, essential to analyze the public sentiment during this wave of the pandemic. While much work prevails to determine the sentiment polarity for tweets related to COVID-19, expressed in the English language, we still need to work on public sentiments expressed in languages other than English. This paper proposes a framework, Covhindia, a deep-learning framework that performs sentiment polarity detection of tweets related to COVID-19 posted in the Hindi language on the Twitter platform. The proposed framework leverages machine translation on Hindi tweets and passes the translated data as input to a deep learning model which is trained on an English corpus of COVID-19 tweets posted from India [18]. The paper compares nine deep learning models' performances in classifying the sentiment polarity on an English dataset. Performance comparison of these architectures reveals that the BERT model had the best polarity detection accuracy on the English corpus. As part of testing the Covhindia’s accuracy in performing sentiment classification on Hindi tweets, the paper employs a separate dataset developed using a python library called Tweepy to extract Hindi tweets related to COVID-19. Experimental results reveal that Covhindia achieved state-of-the-art accuracy in classifying COVID-19 tweets posted in the Hindi language. The use of open-source machine translation tools paved the way for leveraging Covhindia for performing multilingual sentiment classification on COVID-19 tweets. For the benefit of the research community, the code and Jupyter Notebooks related to this paper are available on Github
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Dhabekar, Shweta, and M. D. Patil. "Implementation of Deep Learning Based Sentiment Classification and Product Aspect Analysis." ITM Web of Conferences 40 (2021): 03032. http://dx.doi.org/10.1051/itmconf/20214003032.
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With the increase in E-Commerce businesses in the last decade,the sentiment analysis of product reviews has gained a lot of attention in linguistic research. In literature, the survey depicts the majority of the research done emphasizes on mere polarity identification of the reviews. The proposed system emphasized on classifying the sentiment polarity and the product aspect identification from the reviews. Proposed work experimented with traditional machine learning techniques as well as deep neural networks such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short Term Memory(LSTM) Networks. The proposed system gives a better understanding of these algorithms by comparing the outcomes. The Deep Learning approach in the proposed work successfully provides a mechanism which identifies the review polarity and intensity of the reviews and also analyses the short form words used by people in the reviews. The experimental results in this work, applied on amazon product dataset, shows that the LSTM model works the best for sentiment analysis and intensity of reviews with 93% accuracy. This research work also predicts polarity for short-form word reviews which is the common trend these days while writing the reviews.
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Mejova, Yelena, and Padmini Srinivasan. "Exploring Feature Definition and Selection for Sentiment Classifiers." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (August 3, 2021): 546–49. http://dx.doi.org/10.1609/icwsm.v5i1.14163.
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In this paper, we systematically explore feature definition and selection strategies for sentiment polarity classification. We begin by exploring basic questions, such as whether to use stemming, term frequency versus binary weighting, negation-enriched features, n-grams or phrases. We then move onto more complex aspects including feature selection using frequency-based vocabulary trimming, part-of-speech and lexicon selection (three types of lexicons), as well as using expected Mutual Information (MI). Using three product and movie review datasets of various sizes, we show, for example, that some techniques are more beneficial for larger datasets than the smaller. A classifier trained on only few features ranked high by MI outperformed one trained on all features in large datasets, yet in small dataset this did not prove to be true. Finally, we perform a space and computation cost analysis to further understand the merits of various feature types.
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Khabour, Safaa M., Qasem A. Al-Radaideh, and Dheya Mustafa. "A New Ontology-Based Method for Arabic Sentiment Analysis." Big Data and Cognitive Computing 6, no. 2 (April 29, 2022): 48. http://dx.doi.org/10.3390/bdcc6020048.
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Arabic sentiment analysis is a process that aims to extract the subjective opinions of different users about different subjects since these opinions and sentiments are used to recognize their perspectives and judgments in a particular domain. Few research studies addressed semantic-oriented approaches for Arabic sentiment analysis based on domain ontologies and features’ importance. In this paper, we built a semantic orientation approach for calculating overall polarity from the Arabic subjective texts based on built domain ontology and the available sentiment lexicon. We used the ontology concepts to extract and weight the semantic domain features by considering their levels in the ontology tree and their frequencies in the dataset to compute the overall polarity of a given textual review based on the importance of each domain feature. For evaluation, an Arabic dataset from the hotels’ domain was selected to build the domain ontology and to test the proposed approach. The overall accuracy and f-measure reach 79.20% and 78.75%, respectively. Results showed that the approach outperformed the other semantic orientation approaches, and it is an appealing approach to be used for Arabic sentiment analysis.
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Smadi, Mohammad Al, Islam Obaidat, Mahmoud Al-Ayyoub, Rami Mohawesh, and Yaser Jararweh. "Using Enhanced Lexicon-Based Approaches for the Determination of Aspect Categories and Their Polarities in Arabic Reviews." International Journal of Information Technology and Web Engineering 11, no. 3 (July 2016): 15–31. http://dx.doi.org/10.4018/ijitwe.2016070102.
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Sentiment Analysis (SA) is the process of determining the sentiment of a text written in a natural language to be positive, negative or neutral. It is one of the most interesting subfields of natural language processing (NLP) and Web mining due to its diverse applications and the challenges associated with applying it on the massive amounts of textual data available online (especially, on social networks). Most of the current work on SA focus on the English language and work on the sentence-level or the document-level. This work focuses on the less studied version of SA, which is aspect-based SA (ABSA) for the Arabic language. Specifically, this work considers two ABSA tasks: aspect category determination and aspect category polarity determination, and makes use of the publicly available human annotated Arabic dataset (HAAD) along with its baseline experiments conducted by HAAD providers. In this work, several lexicon-based approaches are presented for the two tasks at hand and show that some of the presented approaches significantly outperforms the best-known result on the given dataset. An enhancement of 9% and 46% were achieved in the tasks aspect category determination and aspect category polarity determination respectively.
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Assiri, Adel, Ahmed Emam, and Hmood Al-Dossari. "Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis." Journal of Information Science 44, no. 2 (January 23, 2017): 184–202. http://dx.doi.org/10.1177/0165551516688143.
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Sentiment analysis (SA) techniques are applied to assess aspects of language that are used to express feelings, evaluations and opinions in areas such as customer sentiment extraction. Most studies have focused on SA techniques for widely used languages such as English, but less attention has been paid to Arabic, particularly the Saudi dialect. Most Arabic SA studies have built systems using supervised approaches that are domain dependent; hence, they achieve low performance when applied to a new domain different from the learning domain, and they require manually labelled training data, which are usually difficult to obtain. In this article, we propose a novel lexicon-based algorithm for Saudi dialect SA that features domain independence. We created an annotated Saudi dialect dataset and built a large-scale lexicon for the Saudi dialect. Then, we developed our weighted lexicon-based algorithm. The proposed algorithm mines the associations between polarity and non-polarity words for the dataset and then weights these words based on their associations. During algorithm development, we also proposed novel rules for handling some linguistic features such as negation and supplication. Several experiments were performed to evaluate the performance of the proposed algorithm.
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Karim, Musarat, Malik Muhammad Saad Missen, Muhammad Umer, Alisha Fida, Ala’ Abdulmajid Eshmawi, Abdullah Mohamed, and Imran Ashraf. "Comprehension of polarity of articles by citation sentiment analysis using TF-IDF and ML classifiers." PeerJ Computer Science 8 (December 13, 2022): e1107. http://dx.doi.org/10.7717/peerj-cs.1107.
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Sentiment analysis has been researched extensively during the last few years, however, the sentiment analysis of citations in a research article is an unexplored research area. Sentiment analysis of citations can provide new applications in bibliometrics and provide insights for a better understanding of scientific knowledge. Citation count, as it is used today to measure the quality of a paper, does not portray the quality of a scientific article, as the article may be cited to indicate its weakness. So determining the polarity of a citation is an important task to quantify the quality of the cited article and ascertain its impact and ranking. This article presents an approach to determine the polarity of the cited article using term frequency-inverse document frequency and machine learning classifiers. To analyze the influence of an imbalanced dataset, several experiments are performed with and without the synthetic minority oversampling technique (SMOTE) and uni-gram and bi-gram term frequency-inverse document frequency (TF-IDF). Results indicate that the proposed methodology achieves high accuracy of 99.0% with the extra tree classifier when trained on SMOTE oversampled dataset and bi-gram features.
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Nguyen, Huyen T. M., Hung V. Nguyen, Quyen T. Ngo, Luong X. Vu, Vu Mai Tran, Bach X. Ngo, and Cuong A. Le. "VLSP SHARED TASK: SENTIMENT ANALYSIS." Journal of Computer Science and Cybernetics 34, no. 4 (January 30, 2019): 295–310. http://dx.doi.org/10.15625/1813-9663/34/4/13160.
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Sentiment analysis is a natural language processing (NLP) task of identifying orextracting the sentiment content of a text unit. This task has become an active research topic since the early 2000s. During the two last editions of the VLSP workshop series, the shared task on Sentiment Analysis (SA) for Vietnamese has been organized in order to provide an objective evaluation measurement about the performance (quality) of sentiment analysis tools, and encouragethe development of Vietnamese sentiment analysis systems, as well as to provide benchmark datasets for this task. The rst campaign in 2016 only focused on the sentiment polarity classication, with a dataset containing reviews of electronic products. The second campaign in 2018 addressed the problem of Aspect Based Sentiment Analysis (ABSA) for Vietnamese, by providing two datasets containing reviews in restaurant and hotel domains. These data are accessible for research purpose via the VLSP website vlsp.org.vn/resources. This paper describes the built datasets as well as the evaluation results of the systems participating to these campaigns.
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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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Alali, Muath, Nurfadhlina Mohd Sharef, Masrah Azrifah Azmi Murad, Hazlina Hamdan, and Nor Azura Husin. "Multitasking Learning Model Based on Hierarchical Attention Network for Arabic Sentiment Analysis Classification." Electronics 11, no. 8 (April 9, 2022): 1193. http://dx.doi.org/10.3390/electronics11081193.
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Limited approaches have been applied to Arabic sentiment analysis for a five-point classification problem. These approaches are based on single task learning with a handcrafted feature, which does not provide robust sentence representation. Recently, hierarchical attention networks have performed outstandingly well. However, when training such models as single-task learning, these models do not exhibit superior performance and robust latent feature representation in the case of a small amount of data, specifically on the Arabic language, which is considered a low-resource language. Moreover, these models are based on single task learning and do not consider the related tasks, such as ternary and binary tasks (cross-task transfer). Centered on these shortcomings, we regard five ternary tasks as relative. We propose a multitask learning model based on hierarchical attention network (MTLHAN) to learn the best sentence representation and model generalization, with shared word encoder and attention network across both tasks, by training three-polarity and five-polarity Arabic sentiment analysis tasks alternately and jointly. Experimental results showed outstanding performance of the proposed model, with high accuracy of 83.98%, 87.68%, and 84.59 on LABR, HARD, and BRAD datasets, respectively, and a minimum macro mean absolute error of 0.632% on the Arabic tweets dataset for five-point Arabic sentiment classification problem.
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WARIN, THIERRY, and WILLIAM SANGER. "THE SPEECHES OF THE EUROPEAN CENTRAL BANK’s PRESIDENTS: AN NLP STUDY." Global Economy Journal 20, no. 02 (June 2020): 2050009. http://dx.doi.org/10.1142/s2194565920500098.
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This paper introduces natural language processing into the study of central banking. It studies the evolution of the ECB’s communication through time, considering its three subsequent presidents (W. Duisenberg, J. C. Trichet and M. Draghi) and the pre- and post-2008 financial crisis era. It helps understand the history of the ECB since its inception. From a methodological standpoint, we study the evolution of the ECB’s speeches. The speech analysis is based on text classification and sentiment/polarity analyses. For that purpose, we have built a unique dataset of the ECB’s speeches. We have coded algorithms to run the text analysis through time. They help us capture the evolution in the ECB’s understanding of the actual economic situation and also measure — for instance — the stress level at the ECB through a polarity analysis through time.
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Andreyestha, Andreyestha, and Agus Subekti. "ANALISA SENTIMENT PADA ULASAN FILM DENGAN OPTIMASI ENSEMBLE LEARNING." Jurnal Informatika 7, no. 1 (April 6, 2020): 15–23. http://dx.doi.org/10.31311/ji.v7i1.6171.
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Dalam dunia hiburan khususnya film, kini situs web ulasan film menjadi media bagi orang-orang untuk memberikan penilaian mengenai seberapa bagus film tersebut. Mereka tidak harus menjadi pakar dalam dunia perfilman untuk menilai kualitas dari film yang mereka saksikan, semua orang dapat memberikan penilaian. Sentimen yang ditemukan dalam komentar, umpan balik atau kritik memberikan indikator yang berguna untuk berbagai tujuan dan dapat dikategorikan berdasarkan polaritas, polaritas tersebut cenderung akan dicari tahu apakah secara keseluruhan positif atau negatif. Algoritma Naïve Bayes dan Random Forest merupakan algoritma yang dapat memberikan hasil analisa klasifikasi sesuai yang diharapkan pada penelitian ini, analisa akan dilakukan dengan membandingkan beberapa kombinasi algoritma untuk diuji pada Polarity Dataset 2.0 dari Cornell University, diantaranya yaitu Algoritma tersebut akan dikombinasikan dengan seleksi fitur Chi Square, Adaboost, dan Voting. Dari hasil pengujian yang didapat algoritma AdaBosst dan Voting mampu meningkatkan akurasi dari metode Naïve Bayes (NB) and Random Forest (RF). Model yang diusulkan dengan Chi Square + Voting 2 (RF + SVM) memiliki nilai akurai 84,6%, dan model ini memiliki nilai akurasi yang lebih tinggi.
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Onyenwe, Ikechukwu, Samuel N. C. Nwagbo Nwagbo, Ebele Onyedinma Onyedinma, Onyedika Ikechukwu-Onyenwe Onyenwe, Chidinma A. Nwafor, and Obinna Agbata. "Location-based Sentiment Analysis of 2019 Nigeria Presidential Election using a Voting Ensemble Approach." International Journal on Natural Language Computing 12, no. 1 (February 27, 2023): 1–22. http://dx.doi.org/10.5121/ijnlc.2023.12101.
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Nigeria president Buhari defeated his closest rival Atiku Abubakar by over 3 million votes. He was issued a Certificate of Return and was sworn in on 29 May 2019. However, there were claims of widespread hoax by the opposition. The sentiment analysis captures the opinions of the masses over social media for global events. In this paper, we use 2019 Nigeria presidential election tweets to perform sentiment analysis through the application of a voting ensemble approach (VEA) in which the predictions from multiple techniques are combined to find the best polarity of a tweet (sentence). This is to determine public views on the 2019 Nigeria Presidential elections and compare them with actual election results. Our sentiment analysis experiment is focused on location-based viewpoints where we used Twitter location data. For this experiment, we live-streamed Nigeria 2019 election tweets via Twitter API to create tweets dataset of 583816 size, pre-processed the data, and applied VEA by utilizing three different Sentiment Classifiers to obtain the choicest polarity of a given tweet. Furthermore, we segmented our tweets dataset into Nigerian states and geopolitical zones, then plotted state-wise and geopolitical-wise user sentiments towards Buhari and Atiku and their political parties. The overall objective of the use of states/geopolitical zones is to evaluate the similarity between the sentiment of location-based tweets compared to actual election results. The results reveal that whereas there are election outcomes that coincide with the sentiment expressed on Twitter social media in most cases as shown by the polarity scores of different locations, there are also some election results where our location analysis similarity test failed.
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Effendi, Fery Ardiansyah, and Yuliant Sibaroni. "Sentiment Classification for Film Reviews by Reducing Additional Introduced Sentiment Bias." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 5 (October 24, 2021): 863–75. http://dx.doi.org/10.29207/resti.v5i5.3400.
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Film business and its individual reviews cannot be separated and film review sites such as IMDb is a credible source of reviews posted in public forums. With IMDb site reviews being unstructured and bias-heavy, classification methods by reducing additional sentiment bias is needed to create a balanced classification with lower polarity bias. Elimination of additional sentiment bias will improve the model as polarity is defined by non-bias method, resulting in models correctly defined which sequences of words is either positive or negative. This research limits the dataset by 50.000 rows of randomly extracted reviews from the IMDb website using dataset preparation methods such as Preprocessing, POS-Tagging, and Word Embeddings. Then preprocessed data is used in classification methods such as ANN, SWN, and SO-Cal. This paper also used bias processing methods such as Hyperparameter Tuning and BPM, with outputs evaluated using Accuracy and PBR metrics. This research yields 77.39 % for ANN, 66.32% for BPM, 75.6% for SO-Cal, and 76.26% for Hybrid classification. Best PBR resulted in two lexicon-based methods on 0.0009 for BPM, and 0.00006 for SO-Cal. More advanced model configuration in ANN can improve the model, and much complex lexicon models will be a future in the research topic.
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Ahanin, Zahra, Maizatul Akmar Ismail, Narinderjit Singh Sawaran Singh, and Ammar AL-Ashmori. "Hybrid Feature Extraction for Multi-Label Emotion Classification in English Text Messages." Sustainability 15, no. 16 (August 18, 2023): 12539. http://dx.doi.org/10.3390/su151612539.
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Emotions are vital for identifying an individual’s attitude and mental condition. Detecting and classifying emotions in Natural Language Processing applications can improve Human–Computer Interaction systems, leading to effective decision making in organizations. Several studies on emotion classification have employed word embedding as a feature extraction method, but they do not consider the sentiment polarity of words. Moreover, relying exclusively on deep learning models to extract linguistic features may result in misclassifications due to the small training dataset. In this paper, we present a hybrid feature extraction model using human-engineered features combined with deep learning based features for emotion classification in English text. The proposed model uses data augmentation, captures contextual information, integrates knowledge from lexical resources, and employs deep learning models, including Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representation and Transformer (BERT), to address the issues mentioned above. The proposed model with hybrid features attained the highest Jaccard accuracy on two of the benchmark datasets, with 68.40% on SemEval-2018 and 53.45% on the GoEmotions dataset. The results show the significance of the proposed technique, and we can conclude that the incorporation of the hybrid features improves the performance of the baseline models.
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Nafan, Muhammad Zidny, and Andika Elok Amalia. "Kecenderungan Tanggapan Masyarakat terhadap Ekonomi Indonesia berbasis Lexicon Based Sentiment Analysis." JURNAL MEDIA INFORMATIKA BUDIDARMA 3, no. 4 (October 6, 2019): 268. http://dx.doi.org/10.30865/mib.v3i4.1283.
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Sentiment analysis aims to find opinions, identify sentiments expressed, and then classify their polarity values. One method of sentiment analysis is Lexicon-based. This study implements the Lexicon based sentiment analysis to analyze the polarity of public responses to the topic of the development of "the Indonesian economy". The dataset is collected from social media from 2017 to 2019. Preprocessing used is folding cases, deleting newline characters, changing non-standard words, deleting mentions, deleting hashtags, removing URL strings, changing word negation, and translating text into English with TextBlob library. Then extract the sentiment values from adjectives, adverbs, nouns, and verbs found in the text. Based on the results of sentiment analysis, it can be seen that there are 63.6% positive responses from the public to the development of the Indonesian economy, 7.4% negative responses, and 29% neutral.
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Lai, Kwun-Ping, Jackie Chun-Sing Ho, and Wai Lam. "Using Latent Fine-Grained Sentiment for Cross-Domain Sentiment Analysis." International Journal of Knowledge-Based Organizations 11, no. 3 (July 2021): 29–45. http://dx.doi.org/10.4018/ijkbo.2021070103.
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The authors investigate the problem task of multi-source cross-domain sentiment classification under the constraint of little labeled data. The authors propose a novel model which is capable of capturing both sentiment terms with strong or weak polarity from various source domains which are useful for knowledge transfer to unlabeled target domain. The authors propose a two-step training strategy with different granularities helping the model to identify sentiment terms with different degrees of sentiment polarity. Specifically, the coarse-grained training step captures the strong sentiment terms from the whole review while the fine-grained training step focuses on the latent fine-grained sentence sentiment which are helpful under the constraint of little labeled data. Experiments on a real-world product review dataset show that the proposed model has a good performance even under the little labeled data constraint.
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Fayyoumi, Ebaa, and Sahar Idwan. "Semantic Partitioning and Machine Learning in Sentiment Analysis." Data 6, no. 6 (June 21, 2021): 67. http://dx.doi.org/10.3390/data6060067.
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This paper investigates sentiment analysis in Arabic tweets that have the presence of Jordanian dialect. A new dataset was collected during the coronavirus disease (COVID-19) pandemic. We demonstrate two models: the Traditional Arabic Language (TAL) model and the Semantic Partitioning Arabic Language (SPAL) model to envisage the polarity of the collected tweets by invoking several, well-known classifiers. The extraction and allocation of numerous Arabic features, such as lexical features, writing style features, grammatical features, and emotional features, have been used to analyze and classify the collected tweets semantically. The partitioning concept was performed on the original dataset by utilizing the hidden semantic meaning between tweets in the SPAL model before invoking various classifiers. The experimentation reveals that the overall performance of the SPAL model competes over and better than the performance of the TAL model due to imposing the genuine idea of semantic partitioning on the collected dataset.
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Musfiroh, Desi, Ulfa Khaira, Pradita Eko Prasetyo Utomo, and Tri Suratno. "Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon." MALCOM: Indonesian Journal of Machine Learning and Computer Science 1, no. 1 (March 6, 2021): 24–33. http://dx.doi.org/10.57152/malcom.v1i1.20.
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Pelaksanaan perkuliahan daring pada berbagai kampus di Indonesia telah dipertegas sejak makin mewabahnya virus corona. Kuliah daring menjadi solusi untuk tetap menjalankan kegiatan belajar-mengajar di tengah masa pandemi. Namun pelaksanaan perkuliahan daring memunculkan berbagai macam opini dalam masyarakat, khususnya di kalangan pelajar. Hal ini juga menimbulkan sikap pro dan kontra dari berbagai pihak. Untuk itu dilakukan penambangan data dari twitter guna menganalisis sentimen terhadap topik “kuliah daring”. Data diklasifikasikan ke dalam 3 kelas, yaitu positif, negatif, dan netral. Penelitian ini dilakukan dengan teknik lexicon-based approach menggunakan InSet Lexicon sebagai kamus kata opini berbahasa Indonesia. Penentuan kelas sentimen untuk setiap kalimat diperoleh dari hasil perhitungan polarity score. Hasil klasifikasi dari 5811 data tweet ternyata mengandung 63.4% tweet negatif, 27.6% tweet positif, dan 8.9% tweet netral. Pengujian hasil klasifikasi dilakukan dengan metode cross-validation serta confusion matrix dengan 80% data latih dan 20% data uji memberikan nilai accuracy 79.2%, precision sebesar 72.9%, recall sebesar 62.8%, dan f-measure sebesar 67.4%.
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Cortis, Keith, and Brian Davis. "A Dataset of Multidimensional and Multilingual Social Opinions for Malta’s Annual Government Budget." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 971–81. http://dx.doi.org/10.1609/icwsm.v15i1.18120.
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This paper presents three high quality social opinion datasets in the socio-economic domain, specifically Malta's annual Government Budgets of 2018, 2019 and 2020. They contain over 6,000 online posts of user-generated content in English and/or Maltese, gathered from newswires and social networking services. These have been annotated for multiple opinion dimensions, namely subjectivity, sentiment polarity, emotion, sarcasm and irony, and in terms of negation, topic and language. These datasets are a valuable resource for developing Opinion Mining tools and Language Technologies, and can be used as a baseline for assessing the state-of-the-art and for developing new advanced analytical methods for Opinion Mining. Moreover, they can be used for policy formulation, policy-making, decision-making and decision-taking. This research can also support similar initiatives in other countries, studies in the socio-economic domain and applied in other areas, such as Politics, Finance, Marketing, Advertising, Sales and Education.
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Kumari, Suman, Basant Agarwal, and Mamta Mittal. "A Deep Neural Network Model for Cross-Domain Sentiment Analysis." International Journal of Information System Modeling and Design 12, no. 2 (April 2021): 1–16. http://dx.doi.org/10.4018/ijismd.2021040101.
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Sentiment analysis is used to detect the opinion/sentiment expressed from the unstructured text. Most of the existing state-of-the-art methods are based on supervised learning, and therefore, a labelled dataset is required to build the model, and it is very difficult task to obtain a labelled dataset for every domain. Cross-domain sentiment analysis is to develop a model which is trained on labelled dataset of one domain, and the performance is evaluated on another domain. The performance of such cross-domain sentiment analysis is still very limited due to presence of many domain-related terms, and the sentiment analysis is a domain-dependent problem in which words changes their polarity depending upon the domain. In addition, cross-domain sentiment analysis model suffers with the problem of large number of out-of-the-vocabulary (unseen words) words. In this paper, the authors propose a deep learning-based approach for cross-domain sentiment analysis. Experimental results show that the proposed approach improves the performance on the benchmark dataset.
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Setyanto, Arief, Arif Laksito, Fawaz Alarfaj, Mohammed Alreshoodi, Kusrini, Irwan Oyong, Mardhiya Hayaty, Abdullah Alomair, Naif Almusallam, and Lilis Kurniasari. "Arabic Language Opinion Mining Based on Long Short-Term Memory (LSTM)." Applied Sciences 12, no. 9 (April 20, 2022): 4140. http://dx.doi.org/10.3390/app12094140.
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Arabic is one of the official languages recognized by the United Nations (UN) and is widely used in the middle east, and parts of Asia, Africa, and other countries. Social media activity currently dominates the textual communication on the Internet and potentially represents people’s views about specific issues. Opinion mining is an important task for understanding public opinion polarity towards an issue. Understanding public opinion leads to better decisions in many fields, such as public services and business. Language background plays a vital role in understanding opinion polarity. Variation is not only due to the vocabulary but also cultural background. The sentence is a time series signal; therefore, sequence gives a significant correlation to the meaning of the text. A recurrent neural network (RNN) is a variant of deep learning where the sequence is considered. Long short-term memory (LSTM) is an implementation of RNN with a particular gate to keep or ignore specific word signals during a sequence of inputs. Text is unstructured data, and it cannot be processed further by a machine unless an algorithm transforms the representation into a readable machine learning format as a vector of numerical values. Transformation algorithms range from the Term Frequency–Inverse Document Frequency (TF-IDF) transform to advanced word embedding. Word embedding methods include GloVe, word2vec, BERT, and fastText. This research experimented with those algorithms to perform vector transformation of the Arabic text dataset. This study implements and compares the GloVe and fastText word embedding algorithms and long short-term memory (LSTM) implemented in single-, double-, and triple-layer architectures. Finally, this research compares their accuracy for opinion mining on an Arabic dataset. It evaluates the proposed algorithm with the ASAD dataset of 55,000 annotated tweets in three classes. The dataset was augmented to achieve equal proportions of positive, negative, and neutral classes. According to the evaluation results, the triple-layer LSTM with fastText word embedding achieved the best testing accuracy, at 90.9%, surpassing all other experimental scenarios.
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Ryoba, Michael J., Shaojian Qu, Ying Ji, and Deqiang Qu. "The Right Time for Crowd Communication during Campaigns for Sustainable Success of Crowdfunding: Evidence from Kickstarter Platform." Sustainability 12, no. 18 (September 16, 2020): 7642. http://dx.doi.org/10.3390/su12187642.
Full textAbstract:
Only a small percentage of crowdfunding projects succeed in securing funds, the fact of which puts the sustainability of crowdfunding platforms at risk. Researchers have examined the influences of phased aspects of communication, drawn from updates and comments, on success of crowdfunding campaigns, but in most cases they have focused on the combined effects of the aspects. This paper investigated campaign success contribution of various combinations of phased communication aspects from updates and comments, the best of which can help creators to successfully manage campaigns by focusing on the important communication aspects. Metaheuristic and machine learning algorithms were used to search and evaluate the best combination of phased communication aspects for predicting success using Kickstarter dataset. The study found that the number of updates in phase one, the polarity of comments in phase two, readability of updates and polarity of comments in phase three, and the polarity of comments in phase five are the most important communication aspects in predicting campaign success. Moreover, the success prediction accuracy with the aspects identified after phasing is more than the baseline model without phasing. Our findings can help crowdfunding actors to focus on the important communication aspects leading to improved likelihood of success.
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Patel, Ravikumar, and Kalpdrum Passi. "Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning." IoT 1, no. 2 (October 10, 2020): 218–39. http://dx.doi.org/10.3390/iot1020014.
Full textAbstract:
In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.
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Gourisaria, Mahendra Kumar, Satish Chandra, Himansu Das, Sudhansu Shekhar Patra, Manoj Sahni, Ernesto Leon-Castro, Vijander Singh, and Sandeep Kumar. "Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies." Healthcare 10, no. 5 (May 10, 2022): 881. http://dx.doi.org/10.3390/healthcare10050881.
Full textAbstract:
The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%.
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Batanović, Vuk, Miloš Cvetanović, and Boško Nikolić. "A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts." PLOS ONE 15, no. 11 (November 12, 2020): e0242050. http://dx.doi.org/10.1371/journal.pone.0242050.
Full textAbstract:
Choosing a comprehensive and cost-effective way of articulating and annotating the sentiment of a text is not a trivial task, particularly when dealing with short texts, in which sentiment can be expressed through a wide variety of linguistic and rhetorical phenomena. This problem is especially conspicuous in resource-limited settings and languages, where design options are restricted either in terms of manpower and financial means required to produce appropriate sentiment analysis resources, or in terms of available language tools, or both. In this paper, we present a versatile approach to addressing this issue, based on multiple interpretations of sentiment labels that encode information regarding the polarity, subjectivity, and ambiguity of a text, as well as the presence of sarcasm or a mixture of sentiments. We demonstrate its use on Serbian, a resource-limited language, via the creation of a main sentiment analysis dataset focused on movie comments, and two smaller datasets belonging to the movie and book domains. In addition to measuring the quality of the annotation process, we propose a novel metric to validate its cost-effectiveness. Finally, the practicality of our approach is further validated by training, evaluating, and determining the optimal configurations of several different kinds of machine-learning models on a range of sentiment classification tasks using the produced dataset.