Sentiment strength detection for the social web

Data Mining: Practical Machine Learning Tools and Techniques

Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works

Content Analysis: An Introduction to its Methodology.

Shelley, M., & Krippendorff, K. (1984) · Journal of the American Statistical Association · doi:10.2307/2288384 · Cited by 24,305 · see citing works

Data Mining

Witten, I. H. (2008) · Cited by 9,115 · see citing works

Opinion Mining and Sentiment Analysis

Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957 · see citing works

Opinion mining and sentiment analysis

Pang, B., & Lee, L. (2008) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000011 · Cited by 6,372 · see citing works

Advances in kernel methods: support vector learning

Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809 · see citing works

Making Large-Scale SVM Learning PracticalPreprint

Joachims, T. (1998) · RePEc: Research Papers in Economics · doi:10.17877/de290r-14262 · Cited by 4,514 · see citing works

Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews

Peter, P., & Turney (2002) · Cited by 3,646 · see citing works

Lexicon-based methods for sentiment analysis

Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011) · Computational Linguistics · doi:10.1162/coli_a_00049 · Cited by 3,301 · see citing works

Twitter as a Corpus for Sentiment Analysis and Opinion Mining

Pak, A., & Paroubek, P. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/47akcj8foo4d · Cited by 2,725 · see citing works

SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining

Baccianella, S., Esuli, A., & Sebastiani, F. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2jktnrykui9s · Cited by 2,691 · see citing works

Psychological Aspects of Natural Language Use: Our Words, Our Selves

Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003) · Annual Review of Psychology · doi:10.1146/annurev.psych.54.101601.145041 · Cited by 2,624 · see citing works

Thumbs up or thumbs down?

Turney, P. D. (2001) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/1073083.1073153 · Cited by 2,354 · see citing works

From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series

O'Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. A. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14031 · Cited by 1,922 · see citing works

Inter-Coder Agreement for Computational Linguistics

Artstein, R., & Poesio, M. (2008) · Computational Linguistics · doi:10.1162/coli.07-034-r2 · Cited by 1,665 · see citing works

Sentiment strength detection in short informal text

Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21416 · Cited by 1,449 · see citing works

Predicting the semantic orientation of adjectives

Hatzivassiloglou, V., & McKeown, K. R. (1997) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/976909.979640 · Cited by 1,445 · see citing works

WordNet Affect: an Affective Extension of WordNet

Strapparava, C., & Valitutti, A. (2004) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2yctm9q26ar4 · Cited by 1,387 · see citing works

The General Inquirer: A Computer Approach to Content Analysis

Green, B. F., Stone, P. J., Dunphy, D. C., Smith, M. S., & Ogilvie, D. M. (1967) · American Educational Research Journal · doi:10.2307/1161774 · Cited by 1,013 · see citing works

Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena

Bollen, J., Mao, H., & Pepe, A. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v5i1.14171 · Cited by 936 · see citing works

Sentiment in Twitter events

Thelwall, M. A., Buckley, K. A., & Paltoglou, G. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21462 · Cited by 817 · see citing works

Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis

Wilson, T., Wiebe, J., & Hoffmann, P. (2009) · Computational Linguistics · doi:10.1162/coli.08-012-r1-06-90 · Cited by 731 · see citing works

The General Inquirer: A Computer Approach to Content Analysis.

Hartman, J. J., Stone, P. J., Dunphy, D. C., Smith, M. S., & Ogilvia, D. M. (1967) · American Sociological Review · doi:10.2307/2092070 · Cited by 723 · see citing works

Proceedings of the 39th Annual Meeting on Association for Computational Linguistics - ACL '01

(2001) · doi:10.3115/1073012 · Cited by 720 · see citing works

Learning to identify emotions in text

Strapparava, C., & Mihalcea, R. F. (2008) · ACM Symposium on Applied Computing · doi:10.1145/1363686.1364052 · Cited by 673 · see citing works

Contextual Valence Shifters

Polanyi, L., & Zaenen, A. (2006) · Springer eBooks · doi:10.1007/1-4020-4102-0_1 · Cited by 524 · see citing works

A survey on sentiment detection of reviews

Tang, H., Tan, S., & Cheng, X. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.02.063 · Cited by 477 · see citing works

Characterizing debate performance via aggregated twitter sentiment

Diakopoulos, N. A., & Shamma, D. A. (2010) · ACM SIGCHI Conference on Human Factors in Computing Systems (CHI) · doi:10.1145/1753326.1753504 · Cited by 472 · see citing works

Pulse: Mining Customer Opinions from Free Text

Gamon, M., Aue, A., Corston-Oliver, S. H., & Ringger, E. K. (2005) · Lecture notes in computer science · doi:10.1007/11552253_12 · Cited by 450 · see citing works

Using Emoticons to reduce Dependency in Machine Learning Techniques for Sentiment Classification

Jonathon, & Read (2005) · Cited by 374 · see citing works

Customizing Sentiment Classifiers to New Domains: a Case Study

Aue, A., & Gamon, M. (2005) · Recent Advances in Natural Language Processing · Cited by 369 · see citing works

Using emoticons to reduce dependency in machine learning techniques for sentiment classification

Read, J. (2005) · doi:10.3115/1628960.1628969 · Cited by 367 · see citing works

Bad news travel fast

Naveed, N., Gottron, T., Kunegis, J., & Alhadi, A. C. (2011) · ACM Web Science Conference (WebSci) · doi:10.1145/2527031.2527052 · Cited by 358 · see citing works

Happiness Is Assortative in Online Social Networks

Bollen, J., Gonçalves, B., Ruan, G.-C., & Mao, H.-N. (2011) · Artificial Life · doi:10.1162/artl_a_00034 · Cited by 294 · see citing works

Data mining emotion in social network communication: Gender differences in MySpace

Thelwall, M. A., Wilkinson, D., & Uppal, S. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21180 · Cited by 280 · see citing works

Automatic construction of a context-aware sentiment lexicon

Lu, Y., Castellanos, M., Umeshwar Dayal, B., & Zhai, C. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963456 · Cited by 267 · see citing works

Feature subsumption for opinion analysis

Riloff, E., Patwardhan, S., & Wiebe, J. (2006) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/1610075.1610137 · Cited by 262 · see citing works

An unobtrusive behavioral model of "gross national happiness"

Kramer, A. D. I. (2010) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1753326.1753369 · Cited by 247 · see citing works

The viability of web-derived polarity lexicons

Velikovich, L., Blair-Goldensohn, S., Hannan, K., & McDonald, R. (2010) · Cited by 227 · see citing works

Twitter, MySpace, Digg

Paltoglou, G., & Thelwall, M. A. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2337542.2337551 · Cited by 215 · see citing works

Sentiment Analysis in the NewsPreprint

Balahur, A., Steinberger, R., Kabadjov, M., Zavarella, V., van der Goot, E., Halkia, M., Pouliquen, B., & Belyaeva, J. (2013) · arXiv (Cornell University) · doi:10.4230/oasics.slate.2021.17 · Cited by 207 · see citing works

Analyzing Appraisal Automatically

Taboada, M., & Grieve, J. (2004) · Cited by 187 · see citing works

SentiFul: A Lexicon for Sentiment Analysis

Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2011) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.1 · Cited by 186 · see citing works

RECOGNIZING STRONG AND WEAK OPINION CLAUSES

Wilson, T., Wiebe, J., & Hwa, R. (2006) · Computational Intelligence · doi:10.1111/j.1467-8640.2006.00275.x · Cited by 185 · see citing works

Negative emotions boost user activity at BBC forum

Chmiel, A., Sobkowicz, P., Sienkiewicz, J., Paltoglou, G., Buckley, K. A., Thelwall, M. A., & Hołyst, J. A. (2011) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2011.03.040 · Cited by 172 · see citing works

Capturing Global Mood Levels using Blog Posts

Mishne, G., de Rijke, M., Nicolov, N., Salvetti, F., Liberman, M., & Martin, J. H. (2006) · UvA-DARE (University of Amsterdam) · Cited by 172 · see citing works

When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging

Andreevskaia, A., & Bergler, S. (2008) · Cited by 164 · see citing works

Textual Affect Sensing for Sociable and Expressive Online Communication

Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74889-2_20 · Cited by 137 · see citing works

A novel scheme for domain-transfer problem in the context of sentiment analysis

Tan, S., Wu, G., Tang, H., & Cheng, X. (2007) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1321440.1321590 · Cited by 113 · see citing works

Is Happiness Contagious Online? A Case of Twitter and the 2010 Winter Olympics

Gruzd, A. A., Doiron, S., & Mai, P. (2011) · Hawaii International Conference on System Sciences (HICSS) · doi:10.1109/hicss.2011.259 · Cited by 91 · see citing works

The Affective Weight of Lexicon

Strapparava, C., Valitutti, A., & Stock, O. (2006) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/3smgxh9anqi3 · Cited by 85 · see citing works

Networks and emotion-driven user communities at popular blogs

Dankulov, M. M., Paltoglou, G., & Tadić, B. (2010) · The European Physical Journal B · doi:10.1140/epjb/e2010-00279-x · Cited by 66 · see citing works

Emotion homophily in social network site messages

Thelwall, M. A. (2010) · First Monday · doi:10.5210/fm.v15i4.2897 · Cited by 57 · see citing works

Genre-based paragraph classification for sentiment analysis

Taboada, M., Brooke, J., & Stede, M. (2009) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.3115/1708376.1708385 · Cited by 54 · see citing works

Exploring the Sentiment Strength of User Reviews

Lu, Y., Kong, X., Quan, X., Liu, W., & Xu, Y. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14246-8_46 · Cited by 47 · see citing works

Determining the Polarity and Source of Opinions Expressed in Political Debates

Balahur, A., Kozareva, Z., & Montoyo, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-00382-0_38 · Cited by 40 · see citing works

Recognition of Fine-Grained Emotions from Text: An Approach Based on the Compositionality Principle

Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2010) · Smart innovation, systems and technologies · doi:10.1007/978-3-642-12604-8_9 · Cited by 22 · see citing works

A Two-Stage Algorithm for Domain Adaptation with Application to Sentiment Transfer Problems

Wu, Q., Tan, S., Duan, M., & Cheng, X. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-17187-1_43 · Cited by 7 · see citing works

Unsupervised and Knowledge-poor Approaches to Sentiment Analysis

Zagibalov, T. (2010) · UPT. Syiah Kuala University Library (Syiah Kuala University) · doi:10.13140/rg.2.1.2488.7846 · Cited by 6 · see citing works

Lexicon-based methods for sentiment analysis

Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011) · Computational Linguistics · doi:10.1162/coli_a_00049 · Cited by 3,301 · see citing works

A survey on opinion mining and sentiment analysis: Tasks, approaches and applications

Ravi, K., & Ravi, V. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.06.015 · Cited by 1,319 · see citing works

Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification

Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., & Qin, B. (2014) · Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.3115/v1/p14-1146 · Cited by 1,181 · see citing works

Survey on Aspect-Level Sentiment Analysis

Schouten, K., & Frăsincar, F. (2015) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2485209 · Cited by 842 · see citing works

Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

Felbo, B., Mislove, A., Søgaard, A., Rahwan, I., & Lehmann, S. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1169 · Cited by 815 · see citing works

A Survey on Text Classification: From Traditional to Deep Learning

Li, Q., Peng, H. M., Li, J., Xia, C., Yang, R., Sun, L., Yu, P. S., & He, L. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3495162 · Cited by 695 · see citing works

How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews

Guzmán, E., & Maalej, W. (2014) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2014.6912257 · Cited by 611 · see citing works

The evolution of sentiment analysis—A review of research topics, venues, and top cited papers

Mäntylä, M. V., Graziotin, D., & Kuutila, M. (2017) · Computer Science Review · doi:10.1016/j.cosrev.2017.10.002 · Cited by 591 · see citing works

Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics

Salehan, M., & Kim, D. J. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.10.006 · Cited by 588 · see citing works

Like It or Not

Giachanou, A., & Crestani, F. (2016) · ACM Computing Surveys · doi:10.1145/2938640 · Cited by 562 · see citing works

Good debt or bad debt: Detecting semantic orientations in economic texts

Malo, P., Sinha, A., Korhonen, P., Wallenius, J., & Takala, P. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23062 · Cited by 513 · see citing works

Deep Convolution Neural Networks for Twitter Sentiment Analysis

Zhao, J., Xiaolin, G., & Zhang, X. (2018) · IEEE Access · doi:10.1109/access.2017.2776930 · Cited by 488 · see citing works

Bug report, feature request, or simply praise? On automatically classifying app reviews

Maalej, W., & Nabil, H. (2015) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2015.7320414 · Cited by 463 · see citing works

Contextual semantics for sentiment analysis of Twitter

Saif, H., He, Y., Fernández, M., & Alani, H. (2015) · Information Processing & Management · doi:10.1016/j.ipm.2015.01.005 · Cited by 423 · see citing works

The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms

van Atteveldt, W., van der Velden, M. A. C. G., & Boukes, M. (2021) · Communication Methods and Measures · doi:10.1080/19312458.2020.1869198 · Cited by 420 · see citing works

Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis

Zhao, J., & Xiaolin, G. (2017) · IEEE Access · doi:10.1109/access.2017.2672677 · Cited by 394 · see citing works

Quantifying controversy on social media

Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2018) · ACM Transactions on Social Computing · doi:10.1145/3140565 · Cited by 348 · see citing works

Digital Emotion Contagion

Goldenberg, A., & Gross, J. J. (2020) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2020.01.009 · Cited by 321 · see citing works

Arabic sentiment analysis: Lexicon-based and corpus-based

Abdulla, N. A., Ahmed, N. A., Shehab, M. A., & Al‐Ayyoub, M. (2013) · IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies · doi:10.1109/aeect.2013.6716448 · Cited by 313 · see citing works

Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies

Kim, Y., Kim, J. G., Kim, W., Im, J., Kim, T. H., Kang, S.-J., & Kim, C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0161197 · Cited by 287 · see citing works

Is Neutral Really Neutral? The Effects of Neutral User-Generated Content on Product Sales

Tang, T., Fang, E., & Wang, F. (2014) · Journal of Marketing · doi:10.1509/jm.13.0301 · Cited by 284 · see citing works

On the automatic classification of app reviews

Maalej, W., Kurtanović, Z., Nabil, H., & Stanik, C. (2016) · Requirements Engineering · doi:10.1007/s00766-016-0251-9 · Cited by 284 · see citing works

WASSA-2017 Shared Task on Emotion Intensity

Mohammad, S. M., & Bravo-Márquez, F. (2017) · Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA) · doi:10.18653/v1/w17-5205 · Cited by 284 · see citing works

A survey of state-of-the-art approaches for emotion recognition in text

Alswaidan, N., & Menaï, M. E. B. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-020-01449-0 · Cited by 279 · see citing works

Sentiment Analysis on Multi-View Social Data

Niu, T., Zhu, S., Pang, L., & Saddik, A. E. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27674-8_2 · Cited by 272 · see citing works

Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons

Khoo, C. S. G., & Johnkhan, S. B. (2017) · Journal of Information Science · doi:10.1177/0165551517703514 · Cited by 270 · see citing works

Network Issue Agendas on Twitter During the 2012 U.S. Presidential Election

Vargo, C. J., Гуо, Л., McCombs, M., & Shaw, D. L. (2014) · Journal of Communication · doi:10.1111/jcom.12089 · Cited by 259 · see citing works

An Integrated Text Analytic Framework for Product Defect Discovery

Abrahams, A. S., Fan, W., Wang, G. A., Zhang, Z., & Jiao, J. (2014) · Production and Operations Management · doi:10.1111/poms.12303 · Cited by 254 · see citing works

Sentiment analysis of commit comments in GitHub: an empirical study

Guzmán, E., Azócar, D., & Yang, L. (2014) · International Conference on Mining Software Repositories (MSR) · doi:10.1145/2597073.2597118 · Cited by 253 · see citing works

Survey on sentiment analysis: evolution of research methods and topics

Cui, J., Wang, Z., Ho, S.-B., & Cambria, E. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10386-z · Cited by 247 · see citing works

An Overview of Sentiment Analysis in Social Media and Its Applications in Disaster Relief

Beigi, G., Hu, X., Maciejewski, R., & Liu, H. (2016) · Studies in computational intelligence · doi:10.1007/978-3-319-30319-2_13 · Cited by 237 · see citing works

Emotion Intensities in Tweets

Mohammad, S. M., & Bravo-Márquez, F. (2017) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s17-1007 · Cited by 236 · see citing works

A novel social media competitive analytics framework with sentiment benchmarks

He, W., Wu, H., Yan, G., Akula, V., & Shen, J. (2015) · Information & Management · doi:10.1016/j.im.2015.04.006 · Cited by 233 · see citing works

The Effect of Social Media Usage Characteristics on e-WOM, Trust, and Brand Equity: Focusing on Users of Airline Social Media

Seo, E. J., Park, J.-W., & Choi, Y. J. (2020) · Sustainability · doi:10.3390/su12041691 · Cited by 232 · see citing works

A comparative evaluation of pre-processing techniques and their interactions for twitter sentiment analysis

Symeonidis, S., Effrosynidis, D., & Arampatzis, A. (2018) · Expert systems with applications · doi:10.1016/j.eswa.2018.06.022 · Cited by 214 · see citing works

A sentiment-enhanced personalized location recommendation system

Yang, D., Zhang, D., Yu, Z., & Wang, Z. (2013) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2481492.2481505 · Cited by 228 · see citing works

Lisbon Emoji and Emoticon Database (LEED): Norms for emoji and emoticons in seven evaluative dimensions

Rodrigues, D. L., Prada, M., Gaspar, R., Garrido, M. V., & Lopes, D. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0878-6 · Cited by 228 · see citing works

Combining lexicon and learning based approaches for concept-level sentiment analysis

Mudinas, A., Zhang, D., & Mark Levene, A. (2012) · International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) · doi:10.1145/2346676.2346681 · Cited by 223 · see citing works

Meta-level sentiment models for big social data analysis

Bravo-Márquez, F., Mendoza, M., & Poblete, B. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.05.016 · Cited by 221 · see citing works

Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data

Kumar, A., Srinivasan, K., Cheng, W.-H., & Zomaya, A. Y. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102141 · Cited by 214 · see citing works

Sentiment analysis of political communication: combining a dictionary approach with crowdcoding

Haselmayer, M., & Jenny, M. (2016) · Quality & Quantity: International Journal of Methodology · doi:10.1007/s11135-016-0412-4 · Cited by 193 · see citing works

A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm

Liu, Y., Bi, J.-W., & Fan, Z. (2017) · Information Sciences · doi:10.1016/j.ins.2017.02.016 · Cited by 211 · see citing works

Deep learning for sentiment analysis: successful approaches and future challenges

Tang, D., Qin, B., & Liu, T. (2015) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1171 · Cited by 209 · see citing works

Sentiment Polarity Detection for Software Development

Calefato, F., Lanubile, F., Maiorano, F., & Novielli, N. (2017) · Empirical Software Engineering · doi:10.1007/s10664-017-9546-9 · Cited by 208 · see citing works

Analyzing and predicting viral tweets

Jenders, M., Kasneci, G., & Naumann, F. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2487788.2488017 · Cited by 206 · see citing works

Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold.

Saif, H., Fernández, M., He, Y., & Alani, H. (2013) · Open Research Online - ORO (The Open University) · Cited by 206 · see citing works

Analytical mapping of opinion mining and sentiment analysis research during 2000–2015

Piryani, R., Madhavi, D., & Singh, V. K. (2016) · Information Processing & Management · doi:10.1016/j.ipm.2016.07.001 · Cited by 205 · see citing works

Multi-class sentiment classification: The experimental comparisons of feature selection and machine learning algorithms

Liu, Y., Bi, J.-W., & Fan, Z. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.03.042 · Cited by 204 · see citing works

Multilingual Twitter Sentiment Classification: The Role of Human Annotators

Mozetič, I., Grćar, M., & Smailović, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155036 · Cited by 203 · see citing works

The Heart and Soul of the Web? Sentiment Strength Detection in the Social Web with SentiStrength

Thelwall, M. A. (2016) · Understanding complex systems · doi:10.1007/978-3-319-43639-5_7 · Cited by 202 · see citing works

Understanding online health information: Evaluation, tools, and strategies

Beaunoyer, E., Arsenault, M., Lomanowska, A. M., & Guitton, M. J. (2016) · Patient Education and Counseling · doi:10.1016/j.pec.2016.08.028 · Cited by 195 · see citing works

Refining Word Embeddings for Sentiment Analysis

Yu, L.-C., Wang, J., Lai, K.-H. R., & Zhang, X. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1056 · Cited by 195 · see citing works

Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews

Mcilroy, S., Ali, N., Khalid, H., & Hassan, A. E. (2015) · Empirical Software Engineering · doi:10.1007/s10664-015-9375-7 · Cited by 193 · see citing works

Application of Machine Learning Methods in Mental Health Detection: A Systematic Review

Rahman, R. A., Omar, K., Noah, S. A. M., Danuri, M. S. N. M., & Al-Garadi, M. A. (2020) · IEEE Access · doi:10.1109/access.2020.3029154 · Cited by 189 · see citing works

Collective Emotions

Goldenberg, A., García, D., Halperin, E., & Gross, J. J. (2020) · Current Directions in Psychological Science · doi:10.1177/0963721420901574 · Cited by 187 · see citing works

Measuring User Engagement

Lalmas, M., O’Brien, H., & Yom-Tov, E. (2015) · Synthesis Lectures on Information Concepts, Retrieval, and Services · doi:10.1007/978-3-031-02289-0 · Cited by 192 · see citing works

Recognizing emotions in text using ensemble of classifiers

Perikos, I., & Hatzilygeroudis, I. (2016) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2016.01.012 · Cited by 176 · see citing works

Sentiment Analysis: From Opinion Mining to Human-Agent Interaction

Clavel, C., & Callejas, Z. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2444846 · Cited by 179 · see citing works

Refining Word Embeddings Using Intensity Scores for Sentiment Analysis

Yu, L.-C., Wang, J., Lai, K.-H. R., & Zhang, X. (2017) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2017.2788182 · Cited by 177 · see citing works

Parallel Aspect‐Oriented Sentiment Analysis for Sales Forecasting with Big Data

Lau, R. Y. K., Zhang, W., & Xu, W. X. (2017) · Production and Operations Management · doi:10.1111/poms.12737 · Cited by 176 · see citing works

Social media content strategy for sport clubs to drive fan engagement

Annamalai, B., Yoshida, M., Varshney, S., Pathak, A. A., & Venugopal, P. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102648 · Cited by 173 · see citing works

What are the characteristics of high-rated apps? A case study on free Android Applications

Tian, Y., Nagappan, M., Lo, D., & Hassan, A. E. (2015) · IEEE International Conference on Software Maintenance (ICSM) · doi:10.1109/icsm.2015.7332476 · Cited by 170 · see citing works

A linguistic signaling model of social support exchange in online health communities

Chen, L., Baird, A. M., & Straub, D. W. (2019) · Decision Support Systems · doi:10.1016/j.dss.2019.113233 · Cited by 168 · see citing works

Big Data and the brave new world of social media research

Schroeder, R. (2014) · Big Data & Society · doi:10.1177/2053951714563194 · Cited by 167 · see citing works

On negative results when using sentiment analysis tools for software engineering research

Jongeling, R., Sarkar, P., Datta, S., & Serebrenik, A. (2017) · Empirical Software Engineering · doi:10.1007/s10664-016-9493-x · Cited by 167 · see citing works

Lexicon based feature extraction for emotion text classification

Bandhakavi, A., Wiratunga, N., Padmanabhan, D., & Massie, S. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.12.009 · Cited by 150 · see citing works

Three-way enhanced convolutional neural networks for sentence-level sentiment classification

Zhang, Y., Zhang, Z., Miao, D., & Wang, J. (2018) · Information Sciences · doi:10.1016/j.ins.2018.10.030 · Cited by 159 · see citing works

Modelling Valence and Arousal in Facebook posts

Preotiuc-Pietro, D., Schwartz, H. A., Park, G. J., Eichstaedt, J., Kern, M. L., Ungar, L., & Shulman, E. (2016) · WASSA@NAACL-HLT · doi:10.18653/v1/w16-0404 · Cited by 187 · see citing works

Investigating the Healthiness of Internet-Sourced Recipes

Trattner, C., & Elsweiler, D. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052573 · Cited by 156 · see citing works

Sentiment analysis through recurrent variants latterly on convolutional neural network of Twitter

Abid, F., Alam, M., Yasir, M., & Chen, L. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2018.12.018 · Cited by 154 · see citing works

Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs

Koto, F., & Rahmaningtyas, G. Y. (2017) · International Conference on Asian Language Processing · doi:10.1109/ialp.2017.8300625 · Cited by 153 · see citing works

A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks

Dashtipour, K. K., Gogate, M., Li, J., Jiang, F., Kong, B., & Hussain, A. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.009 · Cited by 150 · see citing works

Fostering Participant Health Knowledge and Attitudes: An Econometric Study of a Chronic Disease-Focused Online Health Community

Chen, L., Baird, A. M., & Straub, D. W. (2019) · Journal of Management Information Systems · doi:10.1080/07421222.2018.1550547 · Cited by 145 · see citing works

Context-Based Feature Technique for Sarcasm Identification in Benchmark Datasets Using Deep Learning and BERT Model

Eke, C. I., Norman, A. A., & Shuib, L. M. (2021) · IEEE Access · doi:10.1109/access.2021.3068323 · Cited by 144 · see citing works

Social media management strategies for organizational impression management and their effect on public perception

Benthaus, J., Risius, M., & Beck, R. (2016) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2015.12.001 · Cited by 143 · see citing works

Social media analytics for YouTube comments: potential and limitations

Thelwall, M. A. (2017) · International Journal of Social Research Methodology · doi:10.1080/13645579.2017.1381821 · Cited by 143 · see citing works

When Can Social Media Lead Financial Markets?

Zheludev, I., Smith, R. E., & Aste, T. (2014) · Scientific Reports · doi:10.1038/srep04213 · Cited by 141 · see citing works

Using natural language processing to analyse text data in behavioural science

Feuerriegel, S., Maarouf, A., Bär, D., Geissler, D., Schweisthal, J., Pröllochs, N., Robertson, C. E., Rathje, S., Hartmann, J., Mohammad, S. M., et al. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-024-00392-z · Cited by 140 · see citing works

Social Collaborative Viewpoint Regression with Explainable Recommendations

Ren, Z., Liang, S., Li, P., Wang, S., & de Rijke, M. (2017) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3018661.3018686 · Cited by 140 · see citing works

Contextual sentiment analysis for social media genres

Muhammad, A., Wiratunga, N., & Lothian, R. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.05.032 · Cited by 138 · see citing works

Over a decade of social opinion mining: a systematic review

Cortis, K., & Davis, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10030-2 · Cited by 137 · see citing works

Topic‐based sentiment analysis for the social web: The role of mood and issue‐related words

Thelwall, M. A., & Buckley, K. A. (2013) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.22872 · Cited by 136 · see citing works

Visualizing theme park visitors’ emotions using social media analytics and geospatial analytics

Park, S. B., Kim, J., Lee, Y. K., & Ok, C. “. (2020) · Tourism Management · doi:10.1016/j.tourman.2020.104127 · Cited by 134 · see citing works

Using Twitter trust network for stock market analysis

Ruan, Y., Arjan Durresi, A., & Alfantoukh, L. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.01.016 · Cited by 133 · see citing works

User engagement in online N ews: Under the scope of sentiment, interest, affect, and gaze

Arapakis, I., Lalmas, M., Cambazoglu, B. B., Marcos, M., & Jose, J. M. (2014) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23096 · Cited by 131 · see citing works

How to ask for technical help? Evidence-based guidelines for writing questions on Stack Overflow

Calefato, F., Lanubile, F., & Novielli, N. (2017) · Information and Software Technology · doi:10.1016/j.infsof.2017.10.009 · Cited by 130 · see citing works

‘Going negative’: An APPRAISAL analysis of the rhetoric of Donald Trump on Twitter

Ross, A. S., & Caldwell, D. (2019) · Language & Communication · doi:10.1016/j.langcom.2019.09.003 · Cited by 130 · see citing works

Combining strengths, emotions and polarities for boosting Twitter sentiment analysis

Bravo-Márquez, F., Mendoza, M., & Poblete, B. (2013) · International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM) · doi:10.1145/2502069.2502071 · Cited by 129 · see citing works

Emotional persistence in online chatting communities

Garas, A., García, D., Skowron, M., & Schweitzer, F. (2012) · Scientific Reports · doi:10.1038/srep00402 · Cited by 127 · see citing works

Making sense of comments on YouTube educational videos: a self-directed learning perspective

Lee, C. S., Osop, H., Goh, D. H., & Kelni, G. (2017) · Online Information Review · doi:10.1108/oir-09-2016-0274 · Cited by 127 · see citing works

A topic-enhanced word embedding for Twitter sentiment classification

Ren, Y., Wang, R., & Ji, D. (2016) · Information Sciences · doi:10.1016/j.ins.2016.06.040 · Cited by 125 · see citing works

EmoTxt: A toolkit for emotion recognition from text

Calefato, F., Lanubile, F., & Novielli, N. (2017) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw.2017.8272591 · Cited by 123 · see citing works

Valence-based homophily on Twitter: Network Analysis of Emotions and Political Talk in the 2012 Presidential Election

Himelboim, I., Sweetser, K. D., Tinkham, S. F., Cameron, K., Danelo, M., & West, K. E. (2014) · New Media & Society · doi:10.1177/1461444814555096 · Cited by 122 · see citing works

Cyberbullying severity detection: A machine learning approach

Talpur, B. A., & O’Sullivan, D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0240924 · Cited by 122 · see citing works

Application of social media analytics: a case of analyzing online hotel reviews

He, W., Tian, X., Tao, R., Zhang, W., Yan, G., & Akula, V. (2017) · Online Information Review · doi:10.1108/oir-07-2016-0201 · Cited by 121 · see citing works

SentiStrength-SE: Exploiting domain specificity for improved sentiment analysis in software engineering text

Islam, S., & Zibran, M. F. (2018) · Journal of Systems and Software · doi:10.1016/j.jss.2018.08.030 · Cited by 121 · see citing works

Social emotion classification of short text via topic-level maximum entropy model

Rao, Y., Xie, H., Li, J., Jin, F., Wang, F. L., & Li, Q. (2016) · Information & Management · doi:10.1016/j.im.2016.04.005 · Cited by 116 · see citing works

Leveraging Automated Sentiment Analysis in Software Engineering

Islam, M. R., & Zibran, M. F. (2017) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr.2017.9 · Cited by 114 · see citing works

SentiTurkNet: a Turkish polarity lexicon for sentiment analysis

Dehkharghani, R., Saygın, Y., Yanikoglu, B. A., & Oflazer, K. (2015) · Language Resources and Evaluation · doi:10.1007/s10579-015-9307-6 · Cited by 112 · see citing works

Company event popularity for financial markets using Twitter and sentiment analysis

Daniel, M., Neves, R. F., & Horta, N. C. G. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.11.022 · Cited by 111 · see citing works

The Heart and Soul of the Web? Sentiment Strength Detection in the Social Web with SentiStrength

Thelwall, M. A. (2016) · Understanding complex systems · doi:10.1007/978-3-319-43639-5_7 · Cited by 202 · see citing works

Heart and Soul: Sentiment Strength Detection in the Social Web with SentiStrength 1

Thelwall, M. (2013)

Investigation of sentiment strength detection in twitter texts

Orešković, M. (2012) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)

Sentiment strength detection in short informal text

Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21416 · Cited by 1,449 · see citing works

Topic‐based sentiment analysis for the social web: The role of mood and issue‐related words

Thelwall, M. A., & Buckley, K. A. (2013) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.22872 · Cited by 136 · see citing works