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title: "Sentiment & emotion — papers, page 2"
description: "Sentiment & emotion: 472 papers of the Elamur Research library · page 2 of 6 · 26–125, the most cited first."
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# 😊 Sentiment & emotion

472 papers · page 2 of 6 · 26–125 · the most cited first · [all topics](https://md.research.elamur.ai/free/en/topics/)

Opinions, feelings and attitudes in text: what people like, how they feel, what they mean by it.

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26. [**Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification**Duyu Tang, Furu Wei, Nan Yang, et al.*2014 · 1,341 citations*](https://md.research.elamur.ai/free/en/papers/tang-2014-learning-sentiment-specific-word-embedding/)
27. [**SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter**Valerio Basile, Cristina Bosco, Elisabetta Fersini, et al.*2019 · 1,311 citations*](https://md.research.elamur.ai/free/en/papers/basile-2019-semeval-2019-task-5-multilingual/)
28. [**Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts**Cícero dos Santos, Maíra Gatti*2014 · 1,269 citations*](https://md.research.elamur.ai/free/en/papers/santos-2014-deep-convolutional-neural-networks-sentiment/)
29. [**Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification**Li Dong, Furu Wei, Chuanqi Tan, et al.*2014 · 1,211 citations*](https://md.research.elamur.ai/free/en/papers/dong-2014-adaptive-recursive-neural-network-target/)
30. [**Aspect Level Sentiment Classification with Deep Memory Network**Duyu Tang, Bing Qin, Ting Liu*2016 · 1,187 citations*](https://md.research.elamur.ai/free/en/papers/tang-2016-aspect-level-sentiment-classification-deep/)
31. [**SemEval-2017 Task 4: Sentiment Analysis in Twitter**Sara Rosenthal, Noura Farra, Preslav Nakov*2017 · 1,159 citations*](https://md.research.elamur.ai/free/en/papers/rosenthal-2017-semeval-2017-task-4-sentiment/)
32. [**The Risk of Racial Bias in Hate Speech Detection**Maarten Sap, Dallas Card, Saadia Gabriel, et al.*2019 · 1,148 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-risk-racial-bias-hate-speech/)
33. [**Baselines and Bigrams: Simple, Good Sentiment and Topic Classification**Sida Wang, Christopher Manning*2012 · 1,116 citations*](https://md.research.elamur.ai/free/en/papers/wang-2012-baselines-bigrams-simple-good-sentiment/)
34. [**Recurrent Attention Network on Memory for Aspect Sentiment Analysis**Peng Chen, Zhongqian Sun, Lidong Bing, et al.*2017 · 1,113 citations*](https://md.research.elamur.ai/free/en/papers/chen-2017-recurrent-attention-network-memory-aspect/)
35. [**SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2019 · 1,109 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2019-semeval-2019-task-6-identifying/)
36. [**SemEval-2018 Task 1: Affect in Tweets**Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, et al.*2018 · 1,092 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2018-semeval-2018-task-1-affect/)
37. [**SemEval-2016 Task 4: Sentiment Analysis in Twitter**Preslav Nakov, Alan Ritter, Sara Rosenthal, et al.*2016 · 1,081 citations*](https://md.research.elamur.ai/free/en/papers/nakov-2016-semeval-2016-task-4-sentiment/)
38. [**Context-Dependent Sentiment Analysis in User-Generated Videos**Soujanya Poria, Erik Cambria, Devamanyu Hazarika, et al.*2017 · 1,019 citations*](https://md.research.elamur.ai/free/en/papers/poria-2017-context-dependent-sentiment-analysis-user/)
39. [**Automated Hate Speech Detection and the Problem of Offensive Language**Thomas Davidson, Dana Warmsley, Michael Macy, et al.*2017 · 1,007 citations*](https://md.research.elamur.ai/free/en/papers/davidson-2017-automated-hate-speech-detection-problem/)
40. [**Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon**Saif Mohammad, Peter Turney*2010 · 1,005 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2010-emotions-evoked-common-words-phrases/)
41. [**Robust Sentiment Detection on Twitter from Biased and Noisy Data**Luciano Barbosa, Junlan Feng*2010 · 952 citations*](https://md.research.elamur.ai/free/en/papers/barbosa-2010-robust-sentiment-detection-twitter-biased/)
42. [**DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation**Deepanway Ghosal, Navonil Majumder, Soujanya Poria, et al.*2019 · 850 citations*](https://md.research.elamur.ai/free/en/papers/ghosal-2019-dialoguegcn-graph-convolutional-neural-network/)
43. [**Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis**Theresa Wilson, Janyce Wiebe, Paul Hoffmann*2009 · 824 citations*](https://md.research.elamur.ai/free/en/papers/wilson-2009-recognizing-contextual-polarity-exploration-features/)
44. [**Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks**Chen Zhang, Qiuchi Li, Dawei Song*2019 · 807 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-aspect-based-sentiment-classification-aspect/)
45. [**NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews**Svetlana Kiritchenko, Xiaodan Zhu, Colin Cherry, et al.*2014 · 788 citations*](https://md.research.elamur.ai/free/en/papers/kiritchenko-2014-nrc-canada-2014-detecting-aspects/)
46. [**Enhanced Sentiment Learning Using Twitter Hashtags and Smileys**Dmitry Davidov, Oren Tsur, Ari Rappoport*2010 · 776 citations*](https://md.research.elamur.ai/free/en/papers/davidov-2010-enhanced-sentiment-learning-twitter-hashtags/)
47. [**Relational Graph Attention Network for Aspect-based Sentiment Analysis**Kai Wang, Weizhou Shen, Yunyi Yang, et al.*2020 · 775 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-relational-graph-attention-network-aspect/)
48. [**Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter**Zeerak Waseem*2016 · 760 citations*](https://md.research.elamur.ai/free/en/papers/waseem-2016-are-you-racist-or-am/)
49. [**Sarcasm as Contrast between a Positive Sentiment and Negative Situation**Ellen Riloff, Ashequl Qadir, Prafulla Surve, et al.*2013 · 752 citations*](https://md.research.elamur.ai/free/en/papers/riloff-2013-sarcasm-as-contrast-between-positive/)
50. [**The Woman Worked as a Babysitter: On Biases in Language Generation**Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, et al.*2019 · 751 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2019-woman-worked-as-babysitter-biases/)
51. [**Identifying Sarcasm in Twitter: A Closer Look**Roberto González-Ibáñez, Smaranda Muresan, Nina Wacholder*2011 · 742 citations*](https://md.research.elamur.ai/free/en/papers/ibanez-2011-identifying-sarcasm-twitter-closer-look/)
52. [**SemEval-2014 Task 9: Sentiment Analysis in Twitter**Sara Rosenthal, Alan Ritter, Preslav Nakov, et al.*2014 · 741 citations*](https://md.research.elamur.ai/free/en/papers/rosenthal-2014-semeval-2014-task-9-sentiment/)
53. [**A Joint Model of Text and Aspect Ratings for Sentiment Summarization**Ivan Titov, Ryan McDonald*2008 · 729 citations*](https://md.research.elamur.ai/free/en/papers/titov-2008-joint-model-text-aspect-ratings/)
54. [**Effective LSTMs for Target-Dependent Sentiment Classification**Duyu Tang, Bing Qin, Xiaocheng Feng, et al.*2016 · 693 citations*](https://md.research.elamur.ai/free/en/papers/tang-2016-effective-lstms-target-dependent-sentiment/)
55. [**NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets**Saif Mohammad, Svetlana Kiritchenko, Xiaodan Zhu*2013 · 660 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2013-nrc-canada-building-state-art/)
56. [**Detecting Hate Speech on the World Wide Web**William Warner, Julia Hirschberg*2012 · 655 citations*](https://md.research.elamur.ai/free/en/papers/warner-2012-detecting-hate-speech-world-wide/)
57. [**Using Convolutional Neural Networks to Classify Hate-Speech**Björn Gambäck, Utpal Kumar Sikdar*2017 · 647 citations*](https://md.research.elamur.ai/free/en/papers/gamback-2017-convolutional-neural-networks-classify-hate/)
58. [**Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm**Bjarke Felbo, Alan Mislove, Anders Søgaard, et al.*2017 · 617 citations*](https://md.research.elamur.ai/free/en/papers/felbo-2017-millions-emoji-occurrences-learn-any/)
59. [**Transformation Networks for Target-Oriented Sentiment Classification**Xin Li, Lidong Bing, Wai Lam, et al.*2018 · 594 citations*](https://md.research.elamur.ai/free/en/papers/li-2018-transformation-networks-target-oriented-sentiment/)
60. [**Co-Training for Cross-Lingual Sentiment Classification**Xiaojun Wan*2009 · 594 citations*](https://md.research.elamur.ai/free/en/papers/wan-2009-co-training-cross-lingual-sentiment/)
61. [**Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings**Pengfei Liu, Shafiq Joty, Helen Meng*2015 · 589 citations*](https://md.research.elamur.ai/free/en/papers/liu-2015-fine-grained-opinion-mining-recurrent/)
62. [**DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment Analysis**Christos Baziotis, Nikos Pelekis, Christos Doulkeridis*2017 · 587 citations*](https://md.research.elamur.ai/free/en/papers/baziotis-2017-datastories-semeval-2017-task-4/)
63. [**Racial Bias in Hate Speech and Abusive Language Detection Datasets**Thomas Davidson, Debasmita Bhattacharya, Ingmar Weber*2019 · 577 citations*](https://md.research.elamur.ai/free/en/papers/davidson-2019-racial-bias-hate-speech-abusive/)
64. [**Hate Speech Dataset from a White Supremacy Forum**Ona de Gibert, Naiara Perez, Aitor García-Pablos, et al.*2018 · 577 citations*](https://md.research.elamur.ai/free/en/papers/gibert-2018-hate-speech-dataset-white-supremacy/)
65. [**Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems**Svetlana Kiritchenko, Saif Mohammad*2018 · 574 citations*](https://md.research.elamur.ai/free/en/papers/kiritchenko-2018-examining-gender-race-bias-two/)
66. [**ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection**Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, et al.*2022 · 571 citations*](https://md.research.elamur.ai/free/en/papers/hartvigsen-2022-toxigen-large-scale-machine-generated/)
67. [**An Unsupervised Aspect-Sentiment Model for Online Reviews**Samuel Brody, Noemie Elhadad*2010 · 569 citations*](https://md.research.elamur.ai/free/en/papers/brody-2010-unsupervised-aspect-sentiment-model-online/)
68. [**BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis**Hu Xu, Bing Liu, Lei Shu, et al.*2019 · 544 citations*](https://md.research.elamur.ai/free/en/papers/xu-2019-bert-post-training-review-reading/)
69. [**Conversational Memory Network for Emotion Recognition in Dyadic Dialogue Videos**Devamanyu Hazarika, Soujanya Poria, Amir Zadeh, et al.*2018 · 539 citations*](https://md.research.elamur.ai/free/en/papers/hazarika-2018-conversational-memory-network-emotion-recognition/)
70. [**ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection**Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, et al.*2018 · 523 citations*](https://md.research.elamur.ai/free/en/papers/hazarika-2018-icon-interactive-conversational-memory-network/)
71. [**Stance Detection with Bidirectional Conditional Encoding**Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, et al.*2016 · 519 citations*](https://md.research.elamur.ai/free/en/papers/augenstein-2016-stance-detection-bidirectional-conditional-encoding/)
72. [**Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis**Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, et al.*2016 · 514 citations*](https://md.research.elamur.ai/free/en/papers/wang-2016-recursive-neural-conditional-random-fields/)
73. [**Sentiment Analysis in the Era of Large Language Models: A Reality Check**Wenxuan Zhang, Yue Deng, Bing Liu, et al.*2023 · 505 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-sentiment-analysis-era-large-language/)
74. [**Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree**Kai Sun, Richong Zhang, Samuel Mensah, et al.*2019 · 497 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-aspect-level-sentiment-analysis-convolution/)
75. [**SemEval-2015 Task 10: Sentiment Analysis in Twitter**Sara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, et al.*2015 · 490 citations*](https://md.research.elamur.ai/free/en/papers/rosenthal-2015-semeval-2015-task-10-sentiment/)
76. [**EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks**Muhammad Abdul-Mageed, Lyle Ungar*2017 · 486 citations*](https://md.research.elamur.ai/free/en/papers/mageed-2017-emonet-fine-grained-emotion-detection/)
77. [**ASTD: Arabic Sentiment Tweets Dataset**Mahmoud Nabil, Mohamed Aly, Amir Atiya*2015 · 470 citations*](https://md.research.elamur.ai/free/en/papers/nabil-2015-astd-arabic-sentiment-tweets-dataset/)
78. [**SemEval-2018 Task 3: Irony Detection in English Tweets**Cynthia Van Hee, Els Lefever, Véronique Hoste*2018 · 467 citations*](https://md.research.elamur.ai/free/en/papers/hee-2018-semeval-2018-task-3-irony/)
79. [**Learning to Generate Reviews and Discovering Sentiment**Alec Radford, Rafal Jozefowicz, Ilya Sutskever*2017 · 467 citations*](https://md.research.elamur.ai/free/en/papers/radford-2017-learning-generate-reviews-discovering-sentiment/)
80. [**Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model**Yitao Cai, Huiyu Cai, Xiaojun Wan*2019 · 450 citations*](https://md.research.elamur.ai/free/en/papers/cai-2019-multi-modal-sarcasm-detection-twitter/)
81. [**CARER: Contextualized Affect Representations for Emotion Recognition**Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, et al.*2018 · 448 citations*](https://md.research.elamur.ai/free/en/papers/saravia-2018-carer-contextualized-affect-representations-emotion/)
82. [**Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.**Diana Maynard, Mark Greenwood*2014 · 434 citations*](https://md.research.elamur.ai/free/en/papers/maynard-2014-who-cares-about-sarcastic-tweets/)
83. [**Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora**William L. Hamilton, Kevin Clark, Jure Leskovec, et al.*2016 · 431 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2016-inducing-domain-specific-sentiment-lexicons/)
84. [**Phrase Dependency Parsing for Opinion Mining**Yuanbin Wu, Qi Zhang, Xuanjing Huang, et al.*2009 · 422 citations*](https://md.research.elamur.ai/free/en/papers/wu-2009-phrase-dependency-parsing-opinion-mining/)
85. [**Recognizing Stances in Ideological On-Line Debates**Swapna Somasundaran, Janyce Wiebe*2010 · 416 citations*](https://md.research.elamur.ai/free/en/papers/somasundaran-2010-recognizing-stances-ideological-line-debates/)
86. [**Harnessing Context Incongruity for Sarcasm Detection**Aditya Joshi, Vinita Sharma, Pushpak Bhattacharyya*2015 · 415 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2015-harnessing-context-incongruity-sarcasm-detection/)
87. [**SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text**Ankush Chatterjee, Kedhar Nath Narahari, Meghana Joshi, et al.*2019 · 409 citations*](https://md.research.elamur.ai/free/en/papers/chatterjee-2019-semeval-2019-task-3-emocontext/)
88. [**Multilingual and Multi-Aspect Hate Speech Analysis**Nedjma Ousidhoum, Zizheng Lin, Hongming Zhang, et al.*2019 · 408 citations*](https://md.research.elamur.ai/free/en/papers/ousidhoum-2019-multilingual-multi-aspect-hate-speech/)
89. [**The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes**Douwe Kiela, Hamed Firooz, Aravind Mohan, et al.*2020 · 403 citations*](https://md.research.elamur.ai/free/en/papers/kiela-2020-hateful-memes-challenge-detecting-hate/)
90. [**Social Biases in NLP Models as Barriers for Persons with Disabilities**Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, et al.*2020 · 402 citations*](https://md.research.elamur.ai/free/en/papers/hutchinson-2020-social-biases-nlp-models-as/)
91. [**A Unified Generative Framework for Aspect-based Sentiment Analysis**Hang Yan, Junqi Dai, Tuo Ji, et al.*2021 · 396 citations*](https://md.research.elamur.ai/free/en/papers/yan-2021-unified-generative-framework-aspect-based/)
92. [**A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis**Qingnan Jiang, Lei Chen, Ruifeng Xu, et al.*2019 · 393 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2019-challenge-dataset-effective-models-aspect/)
93. [**Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification**Xilun Chen, Yu Sun, Ben Athiwaratkun, et al.*2018 · 393 citations*](https://md.research.elamur.ai/free/en/papers/chen-2018-adversarial-deep-averaging-networks-cross/)
94. [**Towards Emotional Support Dialog Systems**Siyang Liu, Chujie Zheng, Orianna Demasi, et al.*2021 · 391 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-emotional-support-dialog-systems/)
95. [**Position-Aware Tagging for Aspect Sentiment Triplet Extraction**Lu Xu, Hao Li, Wei Lu, et al.*2020 · 370 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-position-aware-tagging-aspect-sentiment/)
96. [**Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations**Peixiang Zhong, Di Wang, Chunyan Miao*2019 · 370 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2019-knowledge-enriched-transformer-emotion-detection/)
97. [**WASSA-2017 Shared Task on Emotion Intensity**Saif Mohammad, Felipe Bravo-Marquez*2017 · 368 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2017-wassa-2017-shared-task-emotion/)
98. [**Latent Hatred: A Benchmark for Understanding Implicit Hate Speech**Mai ElSherief, Caleb Ziems, David Muchlinski, et al.*2021 · 364 citations*](https://md.research.elamur.ai/free/en/papers/elsherief-2021-latent-hatred-benchmark-understanding-implicit/)
99. [**Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence**Chi Sun, Luyao Huang, Xipeng Qiu*2019 · 356 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-utilizing-bert-aspect-based-sentiment/)
100. [**Fracking Sarcasm using Neural Network**Aniruddha Ghosh, Tony Veale*2016 · 350 citations*](https://md.research.elamur.ai/free/en/papers/ghosh-2016-fracking-sarcasm-neural-network/)
101. [**Modelling Context with User Embeddings for Sarcasm Detection in Social Media**Silvio Amir, Byron C. Wallace, Hao Lyu, et al.*2016 · 349 citations*](https://md.research.elamur.ai/free/en/papers/amir-2016-modelling-context-user-embeddings-sarcasm/)
102. [**An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment Analysis**Ruidan He, Wee Sun Lee, Hwee Tou Ng, et al.*2019 · 348 citations*](https://md.research.elamur.ai/free/en/papers/he-2019-interactive-multi-task-learning-network/)
103. [**Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer**Juncen Li, Robin Jia, He He, et al.*2018 · 346 citations*](https://md.research.elamur.ai/free/en/papers/li-2018-delete-retrieve-generate-simple-approach/)
104. [**Directed Acyclic Graph Network for Conversational Emotion Recognition**Weizhou Shen, Siyue Wu, Yunyi Yang, et al.*2021 · 344 citations*](https://md.research.elamur.ai/free/en/papers/shen-2021-directed-acyclic-graph-network-conversational/)
105. [**A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks**Soujanya Poria, Erik Cambria, Devamanyu Hazarika, et al.*2016 · 331 citations*](https://md.research.elamur.ai/free/en/papers/poria-2016-deeper-look-into-sarcastic-tweets/)
106. [**Aspect Sentiment Quad Prediction as Paraphrase Generation**Wenxuan Zhang, Yang Deng, Xin Li, et al.*2021 · 325 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2021-aspect-sentiment-quad-prediction-as/)
107. [**Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)**Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, et al.*2019 · 313 citations*](https://md.research.elamur.ai/free/en/papers/castro-2019-towards-multimodal-sarcasm-detection-obviously/)
108. [**A Transformer-based approach to Irony and Sarcasm detection**Rolandos Alexandros Potamias, Georgios Siolas, Andreas - Georgios Stafylopatis*2019 · 312 citations*](https://md.research.elamur.ai/free/en/papers/potamias-2019-transformer-based-approach-irony-sarcasm/)
109. [**MoEL: Mixture of Empathetic Listeners**Zhaojiang Lin, Andrea Madotto, Jamin Shin, et al.*2019 · 301 citations*](https://md.research.elamur.ai/free/en/papers/lin-2019-moel-mixture-empathetic-listeners/)
110. [**Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach**Jingjing Xu, Xu Sun, Qi Zeng, et al.*2018 · 301 citations*](https://md.research.elamur.ai/free/en/papers/xu-2018-unpaired-sentiment-sentiment-translation-cycled/)
111. [**A Benchmark Dataset for Learning to Intervene in Online Hate Speech**Jing Qian, Anna Bethke, Yinyin Liu, et al.*2019 · 290 citations*](https://md.research.elamur.ai/free/en/papers/qian-2019-benchmark-dataset-learning-intervene-online/)
112. [**Emotion Intensities in Tweets**Saif Mohammad, Felipe Bravo-Marquez*2017 · 290 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2017-emotion-intensities-tweets/)
113. [**Detecting Sarcasm in Multimodal Social Platforms**Rossano Schifanella, Paloma de Juan, Joel Tetreault, et al.*2016 · 289 citations*](https://md.research.elamur.ai/free/en/papers/schifanella-2016-detecting-sarcasm-multimodal-social-platforms/)
114. [**Metaphor as a Medium for Emotion: An Empirical Study**Saif Mohammad, Ekaterina Shutova, Peter Turney*2016 · 285 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2016-metaphor-as-medium-emotion-empirical/)
115. [**Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation**Svetlana Kiritchenko, Saif Mohammad*2017 · 282 citations*](https://md.research.elamur.ai/free/en/papers/kiritchenko-2017-best-worst-scaling-more-reliable/)
116. [**Lexical Influences on the Perception of Sarcasm**Roger Kreuz, Gina Caucci*2007 · 278 citations*](https://md.research.elamur.ai/free/en/papers/kreuz-2007-lexical-influences-perception-sarcasm/)
117. [**MIME: MIMicking Emotions for Empathetic Response Generation**Navonil Majumder, Pengfei Hong, Shanshan Peng, et al.*2020 · 277 citations*](https://md.research.elamur.ai/free/en/papers/majumder-2020-mime-mimicking-emotions-empathetic-response/)
118. [**The perfect solution for detecting sarcasm in tweets #not**Christine Liebrecht, Florian Kunneman, Antal van den Bosch*2013 · 274 citations*](https://md.research.elamur.ai/free/en/papers/liebrecht-2013-perfect-solution-detecting-sarcasm-tweets/)
119. [**SemEval-2007 Task 14: Affective Text**Carlo Strapparava, Rada Mihalcea*2007 · 273 citations*](https://md.research.elamur.ai/free/en/papers/strapparava-2007-semeval-2007-task-14-affective/)
120. [**XLM-T: Multilingual Language Models in Twitter for Sentiment Analysis and Beyond**Francesco Barbieri, Luis Espinosa Anke, Jose Camacho-Collados*2022 · 271 citations*](https://md.research.elamur.ai/free/en/papers/barbieri-2022-xlm-t-multilingual-language-models/)
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