Most cited papers in Sentiment Analysis and Opinion Mining
What are the most cited papers in Sentiment Analysis and Opinion Mining?
The most cited paper in Sentiment Analysis and Opinion Mining is "Convolutional Neural Networks for Sentence Classification" (Yoon Kim, 2014), with 14,059 citations. Next come "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks" (12,029) and "Mining and summarizing customer reviews" (7,770).
Showing 50 of 99 papers. Every paper with more than 888 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Convolutional Neural Networks for Sentence Classification Yoon Kim | 2014 | 14,059 |
| 2 | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks Nils Reimers, Iryna Gurevych | 2019 | 12,029 |
| 3 | Mining and summarizing customer reviews Minqing Hu, Bing Liu | 22 Aug 2004 | 7,770 |
| 4 | Thumbs up? Bo Pang, Lillian Lee, Shivakumar Vaithyanathan | 2002 | 7,061 |
| 5 | Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank Richard Socher, Alex Perelygin, Jean Y. Wu et al. | 2013 | 6,886 |
| 6 | Opinion Mining and Sentiment Analysis Bo Pang, Lillian Lee · Foundations and Trends® in Information Retrieval | 7 Jul 2008 | 6,349 |
| 7 | VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text Cecelia Hutto, Éric Gilbert · Proceedings of the International AAAI Conference on Web and Social Media | 16 May 2014 | 6,006 |
| 8 | The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods Yla Tausczik, James W. Pennebaker · Journal of Language and Social Psychology | 8 Dec 2009 | 5,900 |
| 9 | Twitter mood predicts the stock market Johan Bollen, Huina Mao, Xiao‐Jun Zeng · Journal of Computational Science | 3 Feb 2011 | 5,237 |
| 10 | Distributed Representations of Sentences and Documents Quoc Viet Le, Tomáš Mikolov · arXiv (Cornell University) | 16 May 2014 | 5,119 |
| 11 | Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing Pengfei Liu, Weizhe Yuan, Jinlan Fu et al. · ACM Computing Surveys | 14 Sep 2022 | 3,878 |
| 12 | A Convolutional Neural Network for Modelling Sentences Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom | 2014 | 3,590 |
| 13 | Deep Neural Networks for YouTube Recommendations Paul Covington, Jay Adams, Emre Sargin | Sep 2016 | 3,536 |
| 14 | Recognizing contextual polarity in phrase-level sentiment analysis Theresa Wilson, Janyce Wiebe, Paul Hoffmann | 2005 | 3,390 |
| 15 | A sentimental education Bo Pang, Lillian Lee | 2004 | 3,363 |
| 16 | Lexicon-Based Methods for Sentiment Analysis Maite Taboada, Julian Brooke, Milan Tofiloski et al. · Computational Linguistics | 5 Apr 2011 | 3,311 |
| 17 | Sentiment Analysis and Opinion Mining Bing Liu · Synthesis lectures on human language technologies | 23 May 2012 | 3,296 |
| 18 | Learning Word Vectors for Sentiment Analysis Andrew L. Maas, Raymond E. Daly, Peter T. Pham et al. | 19 Jun 2011 | 3,286 |
| 19 | Sentiment analysis algorithms and applications: A survey Walaa Medhat, Ahmed Hassan, Hoda Korashy · Ain Shams Engineering Journal | 27 May 2014 | 3,279 |
| 20 | Twitter as a Corpus for Sentiment Analysis and Opinion Mining Alexander Pak, Patrick Paroubek | May 2010 | 2,725 |
| 21 | SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining Stefano Baccianella, Andrea Esuli, Fabrizio Sebastiani | May 2010 | 2,692 |
| 22 | Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment Andranik Tumasjan, Timm O. Sprenger, Philipp Sandner et al. · Proceedings of the International AAAI Conference on Web and Social Media | 16 May 2010 | 2,691 |
| 23 | CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON Saif M. Mohammad, Peter D. Turney · Computational Intelligence | 4 Sep 2012 | 2,682 |
| 24 | SimCSE: Simple Contrastive Learning of Sentence Embeddings Tianyu Gao, Xingcheng Yao, Danqi Chen · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing | 2021 | 2,679 |
| 25 | SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining Andrea Esuli, Fabrizio Sebastiani | May 2006 | 2,497 |
| 26 | Attention-based LSTM for Aspect-level Sentiment Classification Yequan Wang, Minlie Huang, Xiaoyan Zhu et al. | 2016 | 2,394 |
| 27 | Thumbs up? Sentiment Classification using Machine Learning Techniques Bo Pang, Lillian Lee, Shivakumar Vaithyanathan · arXiv (Cornell University) | 28 May 2002 | 2,214 |
| 28 | Linguistic Inquiry and Word Count (LIWC) Cindy K. Chung, James W. Pennebaker · IGI Global eBooks | 2012 | 2,214 |
| 29 | Seeing stars Bo Pang, Lillian Lee | 2005 | 2,173 |
| 30 | Norms of valence, arousal, and dominance for 13,915 English lemmas Amy Beth Warriner, Victor Kuperman, Marc Brysbaert · Behavior Research Methods | 12 Feb 2013 | 2,024 |
| 31 | From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series Brendan T. O’Connor, Ramnath Balasubramanyan, Bryan Routledge et al. · Proceedings of the International AAAI Conference on Web and Social Media | 16 May 2010 | 1,969 |
| 32 | Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey Hamed Jelodar, Yongli Wang, Chi Yuan et al. · Multimedia Tools and Applications | 27 Nov 2018 | 1,943 |
| 33 | Deep learning for sentiment analysis: A survey Lei Zhang, Shuai Wang, Bing Liu · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery | 30 Mar 2018 | 1,932 |
| 34 | SemEval-2014 Task 4: Aspect Based Sentiment Analysis Maria Pontiki, Dimitrios Galanis, John Pavlopoulos et al. | 2014 | 1,930 |
| 35 | Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications Rafael A. Calvo, Sidney K. D’Mello · IEEE Transactions on Affective Computing | 2010 | 1,874 |
| 36 | Deep Contextualized Word Representations Matthew E. Peters, Mark E Neumann, Mohit Iyyer et al. | 2018 | 1,796 |
| 37 | Annotating Expressions of Opinions and Emotions in Language Janyce Wiebe, Theresa Wilson, Claire Cardie · Computers and the Humanities | May 2005 | 1,781 |
| 38 | Extracting product features and opinions from reviews Ana-Maria Popescu, Oren Etzioni | 2005 | 1,717 |
| 39 | Twitter power: Tweets as electronic word of mouth Bernard J. Jim Jansen, Mimi Zhang, Kate Sobel et al. · Journal of the American Society for Information Science and Technology | 6 Jul 2009 | 1,653 |
| 40 | A review of affective computing: From unimodal analysis to multimodal fusion Soujanya Poria, Erik Cambria, Rajiv Bajpai et al. · Information Fusion | 3 Feb 2017 | 1,644 |
| 41 | Coronavirus Pandemic (COVID-19) Jalal Suliman Alowibdi, Abdulrahman A. Alshdadi, Ali Yusny Daud et al. · International Journal on Semantic Web and Information Systems | Apr 2021 | 1,618 |
| 42 | Hidden factors and hidden topics Julian McAuley, Jure Leskovec | 12 Oct 2013 | 1,614 |
| 43 | Sentiment Analysis and Subjectivity Liu Bing | 2010 | 1,592 |
| 44 | Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews Peter D. Turney · arXiv (Cornell University) | 11 Dec 2002 | 1,585 |
| 45 | Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach Xavier Glorot, Antoine Bordes, Yoshua Bengio | 2011 | 1,560 |
| 46 | Document Modeling with Gated Recurrent Neural Network for Sentiment Classification Duyu Tang, Bing Qin, Ting Liu | 2015 | 1,553 |
| 47 | Deep Learning--based Text Classification Shervin Minaee, Nal Kalchbrenner, Erik Cambria et al. · ACM Computing Surveys | 17 Apr 2021 | 1,536 |
| 48 | Measuring praise and criticism Peter D. Turney, Michael L. Littman · ACM Transactions on Information Systems | Oct 2003 | 1,515 |
| 49 | Techniques and applications for sentiment analysis Ronen Feldman · Communications of the ACM | 25 Mar 2013 | 1,512 |
| 50 | A survey on sentiment analysis methods, applications, and challenges Mayur Wankhade, Annavarapu Chandra Sekhara Rao, Chaitanya Kulkarni · Artificial Intelligence Review | 7 Feb 2022 | 1,509 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.