---
title: "Experimental and Cognitive Psychology — papers, page 2"
description: "Experimental and Cognitive Psychology: 343 papers of the Elamur Research library · page 2 of 5 · 26–125, the most cited first — our summary of each, its…"
canonical: "https://research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/page/2/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/page/2/)

[home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/) / [Experimental and Cognitive Psychology](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) / page 2

# 💭 Experimental and Cognitive Psychology

343 papers · page 2 of 5 · 26–125 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

Experimental and Cognitive Psychology is a subfield of Psychology in the OpenAlex classification of science. The topics most of its papers here are on: «Emotion and Mood Recognition», «Categorization, perception, and language», «Phonetics and Phonology Research».

## Papers

[← prev](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) [1](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) **2** [3](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/page/3/) [4](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/page/4/) [5](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/page/5/) [next →](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/page/3/)

26. [**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/)
27. [**Quantifying Mental Health Signals in Twitter**Glen Coppersmith, Mark Dredze, Craig Harman*2014 · 821 citations*](https://md.research.elamur.ai/free/en/papers/coppersmith-2014-quantifying-mental-health-signals-twitter/)
28. [**Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words**Saif Mohammad*2018 · 800 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2018-obtaining-reliable-human-ratings-valence/)
29. [**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/)
30. [**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/)
31. [**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/)
32. [**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/)
33. [**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/)
34. [**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/)
35. [**Classifying Relations by Ranking with Convolutional Neural Networks**Cícero dos Santos, Bing Xiang, Bowen Zhou*2015 · 613 citations*](https://md.research.elamur.ai/free/en/papers/santos-2015-classifying-relations-ranking-convolutional-neural/)
36. [**Role-Play with Large Language Models**Murray Shanahan, Kyle McDonell, Laria Reynolds*2023 · 606 citations*](https://md.research.elamur.ai/free/en/papers/shanahan-2023-role-play-large-language-models/)
37. [**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/)
38. [**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/)
39. [**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/)
40. [**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/)
41. [**Multi-Prototype Vector-Space Models of Word Meaning**Joseph Reisinger, Raymond J. Mooney*2010 · 489 citations*](https://md.research.elamur.ai/free/en/papers/reisinger-2010-multi-prototype-vector-space-models/)
42. [**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/)
43. [**Learning Multilingual Subjective Language via Cross-Lingual Projections**Rada Mihalcea, Carmen Banea, Janyce Wiebe*2007 · 482 citations*](https://md.research.elamur.ai/free/en/papers/mihalcea-2007-learning-multilingual-subjective-language-cross/)
44. [**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/)
45. [**Distributional Semantics in Technicolor**Elia Bruni, Gemma Boleda, Marco Baroni, et al.*2012 · 459 citations*](https://md.research.elamur.ai/free/en/papers/bruni-2012-distributional-semantics-technicolor/)
46. [**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/)
47. [**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/)
48. [**CLPsych 2015 Shared Task: Depression and PTSD on Twitter**Glen Coppersmith, Mark Dredze, Craig Harman, et al.*2015 · 410 citations*](https://md.research.elamur.ai/free/en/papers/coppersmith-2015-clpsych-2015-shared-task-depression/)
49. [**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/)
50. [**A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support**Ashish Sharma, Adam Miner, David Atkins, et al.*2020 · 408 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2020-computational-approach-understanding-empathy-expressed/)
51. [**URIEL and lang2vec: Representing languages as typological, geographical, and phylogenetic vectors**Patrick Littell, David R. Mortensen, Ke Lin, et al.*2017 · 394 citations*](https://md.research.elamur.ai/free/en/papers/littell-2017-uriel-lang2vec-representing-languages-as/)
52. [**Literal and Metaphorical Sense Identification through Concrete and Abstract Context**Peter Turney, Yair Neuman, Dan Assaf, et al.*2011 · 394 citations*](https://md.research.elamur.ai/free/en/papers/turney-2011-literal-metaphorical-sense-identification-through/)
53. [**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/)
54. [**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/)
55. [**Metaphor Detection with Cross-Lingual Model Transfer**Yulia Tsvetkov, Leonid Boytsov, Anatole Gershman, et al.*2014 · 369 citations*](https://md.research.elamur.ai/free/en/papers/tsvetkov-2014-metaphor-detection-cross-lingual-model/)
56. [**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/)
57. [**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/)
58. [**Language Is Not All You Need: Aligning Perception with Language Models**Shaohan Huang, Li Dong, Wenhui Wang, et al.*2023 · 319 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-language-all-need-aligning-perception/)
59. [**Faith and Fate: Limits of Transformers on Compositionality**Nouha Dziri, Ximing Lu, Melanie Sclar, et al.*2023 · 313 citations*](https://md.research.elamur.ai/free/en/papers/dziri-2023-faith-fate-limits-transformers-compositionality/)
60. [**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/)
61. [**Black Holes and White Rabbits: Metaphor Identification with Visual Features**Ekaterina Shutova, Douwe Kiela, Jean Maillard*2016 · 292 citations*](https://md.research.elamur.ai/free/en/papers/shutova-2016-black-holes-white-rabbits-metaphor/)
62. [**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/)
63. [**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/)
64. [**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/)
65. [**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/)
66. [**Similarity of Semantic Relations**Peter D. Turney*2006 · 277 citations*](https://md.research.elamur.ai/free/en/papers/turney-2006-similarity-semantic-relations/)
67. [**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/)
68. [**CorMet: A Computational, Corpus-Based Conventional Metaphor Extraction System**Zachary J. Mason*2004 · 270 citations*](https://md.research.elamur.ai/free/en/papers/mason-2004-cormet-computational-corpus-based-conventional/)
69. [**met*: A Method for Discriminating Metonymy and Metaphor by Computer**Dan Fass*1991 · 269 citations*](https://md.research.elamur.ai/free/en/papers/fass-1991-met-method-discriminating-metonymy-metaphor/)
70. [**SemEval-2020 Task 8: Memotion Analysis- the Visuo-Lingual Metaphor!**Chhavi Sharma, Deepesh Bhageria, William Scott, et al.*2020 · 260 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2020-semeval-2020-task-8-memotion/)
71. [**Experiments on the Automatic Induction of German Semantic Verb Classes**Schulte im Walde, Sabine*2006 · 259 citations*](https://md.research.elamur.ai/free/en/papers/walde-2006-experiments-automatic-induction-german-semantic/)
72. [**Metaphor Identification Using Verb and Noun Clustering**Ekaterina Shutova, Lin Sun, Anna Korhonen*2010 · 250 citations*](https://md.research.elamur.ai/free/en/papers/shutova-2010-metaphor-identification-verb-noun-clustering/)
73. [**Learning to Distinguish Hypernyms and Co-Hyponyms**Julie Weeds, Daoud Clarke, Jeremy Reffin, et al.*2014 · 242 citations*](https://md.research.elamur.ai/free/en/papers/weeds-2014-learning-distinguish-hypernyms-co-hyponyms/)
74. [**Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text**Bharathi Raja Chakravarthi, Vigneshwaran Muralidaran, Ruba Priyadharshini, et al.*2020 · 234 citations*](https://md.research.elamur.ai/free/en/papers/chakravarthi-2020-corpus-creation-sentiment-analysis-code/)
75. [**Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks**Paul Röttger, Bertie Vidgen, Dirk Hovy, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2022-two-contrasting-data-annotation-paradigms/)
76. [**Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding**Hang Zhang, Xin Li, Lidong Bing*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-video-llama-instruction-tuned-audio/)
77. [**Inclusive yet Selective: Supervised Distributional Hypernymy Detection**Stephen Roller, Katrin Erk, Gemma Boleda*2014 · 217 citations*](https://md.research.elamur.ai/free/en/papers/roller-2014-inclusive-yet-selective-supervised-distributional/)
78. [**Automatic Sarcasm Detection: A Survey**Aditya Joshi, Pushpak Bhattacharyya, Mark James Carman*2016 · 214 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2016-automatic-sarcasm-detection-survey/)
79. [**Nonliteral understanding of number words**Justine T. Kao, Jean Y. Wu, Leon Bergen, et al.*2014 · 213 citations*](https://md.research.elamur.ai/free/en/papers/kao-2014-nonliteral-understanding-number-words/)
80. [**Neural Metaphor Detection in Context**Ge Gao, Eunsol Choi, Yejin Choi, et al.*2018 · 198 citations*](https://md.research.elamur.ai/free/en/papers/gao-2018-neural-metaphor-detection-context/)
81. [**Semi-Supervised Recognition of Sarcasm in Twitter and Amazon**Dmitry Davidov, Oren Tsur, Ari Rappoport*2010 · 198 citations*](https://md.research.elamur.ai/free/en/papers/davidov-2010-semi-supervised-recognition-sarcasm-twitter/)
82. [**A General Framework for Distributional Similarity**Julie Weeds, David Weir*2003 · 198 citations*](https://md.research.elamur.ai/free/en/papers/weeds-2003-general-framework-distributional-similarity/)
83. [**Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models**Haim Dubossarsky, Daphna Weinshall, Eitan Grossman*2017 · 196 citations*](https://md.research.elamur.ai/free/en/papers/dubossarsky-2017-outta-control-laws-semantic-change/)
84. [**Statistical Metaphor Processing**Ekaterina Shutova, Simone Teufel, Anna Korhonen*2013 · 196 citations*](https://md.research.elamur.ai/free/en/papers/shutova-2013-statistical-metaphor-processing/)
85. [**Modeling Empathy and Distress in Reaction to News Stories**Sven Buechel, Anneke Buffone, Barry Slaff, et al.*2018 · 181 citations*](https://md.research.elamur.ai/free/en/papers/buechel-2018-modeling-empathy-distress-reaction-news/)
86. [**Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis**Dushyant Singh Chauhan, Dhanush S R, Asif Ekbal, et al.*2020 · 173 citations*](https://md.research.elamur.ai/free/en/papers/chauhan-2020-sentiment-emotion-help-sarcasm-multi/)
87. [**Modeling English Past Tense Intuitions with Minimal Generalization**Adam Albright, Bruce Hayes*2002 · 173 citations*](https://md.research.elamur.ai/free/en/papers/albright-2002-modeling-english-past-tense-intuitions/)
88. [**A Report on the 2018 VUA Metaphor Detection Shared Task**Chee Wee (Ben) Leong, Beata Beigman Klebanov, Ekaterina Shutova*2018 · 167 citations*](https://md.research.elamur.ai/free/en/papers/leong-2018-report-2018-vua-metaphor-detection/)
89. [**Epitran: Precision G2P for Many Languages**David R. Mortensen, Siddharth Dalmia, Patrick Littell*2018 · 167 citations*](https://md.research.elamur.ai/free/en/papers/mortensen-2018-epitran-precision-g2p-many-languages/)
90. [**Metaphor Corpus Annotated for Source - Target Domain Mappings**Ekaterina Shutova, Simone Teufel*2010 · 164 citations*](https://md.research.elamur.ai/free/en/papers/shutova-2010-metaphor-corpus-annotated-source-target/)
91. [**Emotions from Text: Machine Learning for Text-based Emotion Prediction**Cecilia Ovesdotter Alm, Dan Roth, Richard Sproat*2005 · 160 citations*](https://md.research.elamur.ai/free/en/papers/alm-2005-emotions-text-machine-learning-text/)
92. [**A Sentiment Analysis Dataset for Code-Mixed Malayalam-English**Bharathi Raja Chakravarthi, Navya Jose, Shardul Suryawanshi, et al.*2020 · 158 citations*](https://md.research.elamur.ai/free/en/papers/chakravarthi-2020-sentiment-analysis-dataset-code-mixed/)
93. [**Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding**Will Monroe, Robert X.D. Hawkins, Noah D. Goodman, et al.*2017 · 155 citations*](https://md.research.elamur.ai/free/en/papers/monroe-2017-colors-context-pragmatic-neural-model/)
94. [**LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech**Heiga Zen, Viet Dang, Rob Clark, et al.*2019 · 151 citations*](https://md.research.elamur.ai/free/en/papers/zen-2019-libritts-corpus-derived-librispeech-text/)
95. [**Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network**Abhijit Mishra, Kuntal Dey, Pushpak Bhattacharyya*2017 · 139 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2017-learning-cognitive-features-gaze-data/)
96. [**A Report on the 2020 VUA and TOEFL Metaphor Detection Shared Task**Chee Wee (Ben) Leong, Beata Beigman Klebanov, Chris Hamill, et al.*2020 · 138 citations*](https://md.research.elamur.ai/free/en/papers/leong-2020-report-2020-vua-toefl-metaphor/)
97. [**Experimenting with the king of France: Topics, verifiability and definite descriptions**Márta Abrusán, Kriszta Szendrői*2013 · 132 citations*](https://md.research.elamur.ai/free/en/papers/abrusan-2013-experimenting-king-france-topics-verifiability/)
98. [**Annotation, Modelling and Analysis of Fine-Grained Emotions on a Stance and Sentiment Detection Corpus**Hendrik Schuff, Jeremy Barnes, Julian Mohme, et al.*2017 · 129 citations*](https://md.research.elamur.ai/free/en/papers/schuff-2017-annotation-modelling-analysis-fine-grained/)
99. [**The FrameNet tagset for frame-semantic and syntactic coding of predicate-argument structure**Christopher Johnson, Charles J. Fillmore*2000 · 115 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2000-framenet-tagset-frame-semantic-syntactic/)
100. [**Word Affect Intensities**Saif Mohammad*2018 · 114 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2018-word-affect-intensities/)
101. [**Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes**Hyunwoo Kim, Byeongchang Kim, Gunhee Kim*2021 · 90 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-perspective-taking-pragmatics-generating-empathetic/)
102. [**Get that into your head: Tigre vowel harmonies as templatic**Noam Faust*2017 · 89 citations*](https://md.research.elamur.ai/free/en/papers/faust-2017-get-that-into-your-head/)
103. [**Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series ‘Friends'**Aditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, et al.*2016 · 87 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2016-harnessing-sequence-labeling-sarcasm-detection/)
104. [**Harnessing Cognitive Features for Sarcasm Detection**Abhijit Mishra, Diptesh Kanojia, Seema Nagar, et al.*2016 · 85 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2016-harnessing-cognitive-features-sarcasm-detection/)
105. [**Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations**Gavin Abercrombie, Dirk Hovy*2016 · 85 citations*](https://md.research.elamur.ai/free/en/papers/abercrombie-2016-putting-sarcasm-detection-into-context/)
106. [**A Neural Network Approach to Selectional Preference Acquisition**Van de Cruys, Tim*2014 · 79 citations*](https://md.research.elamur.ai/free/en/papers/cruys-2014-neural-network-approach-selectional-preference/)
107. [**Towards Emotion-aided Multi-modal Dialogue Act Classification**Tulika Saha, Aditya Patra, Sriparna Saha, et al.*2020 · 78 citations*](https://md.research.elamur.ai/free/en/papers/saha-2020-emotion-aided-multi-modal-dialogue/)
108. [**Multi-task dialog act and sentiment recognition on Mastodon**Christophe Cerisara, Somayeh Jafaritazehjani, Adedayo Oluokun, et al.*2018 · 66 citations*](https://md.research.elamur.ai/free/en/papers/cerisara-2018-multi-task-dialog-act-sentiment/)
109. [**ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets**Ramy Baly, Alaa Khaddaj, Hazem Hajj, et al.*2019 · 60 citations*](https://md.research.elamur.ai/free/en/papers/baly-2019-arsentd-lev-multi-topic-corpus/)
110. [**An Affect-Enriched Dialogue Act Classification Model for Task-Oriented Dialogue**Kristy Boyer, Joseph Grafsgaard, Eun Young Ha, et al.*2011 · 60 citations*](https://md.research.elamur.ai/free/en/papers/boyer-2011-affect-enriched-dialogue-act-classification/)
111. [**FrameNet: A Knowledge Base for Natural Language Processing**Collin F. Baker*2014 · 56 citations*](https://md.research.elamur.ai/free/en/papers/baker-2014-framenet-knowledge-base-natural-language/)
112. [**A preference semantics for imperatives**William B Starr*2020 · 55 citations*](https://md.research.elamur.ai/free/en/papers/starr-2020-preference-semantics-imperatives/)
113. [**Metaphor Understanding Challenge Dataset for LLMs**Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-metaphor-understanding-challenge-dataset-llms/)
114. [**Mining Subjective Knowledge from Customer Reviews: A Specific Case of Irony Detection**Antonio Reyes, Paolo Rosso*2011 · 46 citations*](https://md.research.elamur.ai/free/en/papers/reyes-2011-mining-subjective-knowledge-customer-reviews/)
115. [**Annotating Sentiment and Irony in the Online Italian Political Debate on #labuonascuola**Marco Stranisci, Cristina Bosco, Delia Irazú Hernández Farías, et al.*2016 · 45 citations*](https://md.research.elamur.ai/free/en/papers/stranisci-2016-annotating-sentiment-irony-online-italian/)
116. [**Abstraction and Generalisation in Semantic Role Labels: PropBank, VerbNet or both?**Paola Merlo, Lonneke van der Plas*2009 · 45 citations*](https://md.research.elamur.ai/free/en/papers/merlo-2009-abstraction-generalisation-semantic-role-labels/)
117. [**Outsourcing FrameNet to the Crowd**Marco Fossati, Claudio Giuliano, Sara Tonelli*2013 · 44 citations*](https://md.research.elamur.ai/free/en/papers/fossati-2013-outsourcing-framenet-crowd/)
118. [**What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability**Yaochen Liu, Yazhou Zhang, Qiuchi Li, et al.*2021 · 43 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-what-does-your-smile-mean/)
119. [**Anticausatives are semantically reflexive in Norwegian, but not in English**Björn Lundquist, Martin Corley, Mai Tungseth, et al.*2016 · 41 citations*](https://md.research.elamur.ai/free/en/papers/lundquist-2016-anticausatives-are-semantically-reflexive-norwegian/)
120. [**ASAD: A Twitter-based Benchmark Arabic Sentiment Analysis Dataset**Basma Alharbi, Hind Alamro, Manal Alshehri, et al.*2020 · 39 citations*](https://md.research.elamur.ai/free/en/papers/alharbi-2020-asad-twitter-based-benchmark-arabic/)
121. [**Talking about the weather: Two construals of precipitation events in English**Beth Levin, Bonnie Krejci*2019 · 38 citations*](https://md.research.elamur.ai/free/en/papers/levin-2019-talking-about-weather-two-construals/)
122. [**Reactive Supervision: A New Method for Collecting Sarcasm Data**Boaz Shmueli, Lun-Wei Ku, Soumya Ray*2020 · 36 citations*](https://md.research.elamur.ai/free/en/papers/shmueli-2020-reactive-supervision-new-method-collecting/)
123. [**A Multimodal Corpus for Emotion Recognition in Sarcasm**Anupama Ray, Shubham Mishra, Apoorva Nunna, et al.*2022 · 35 citations*](https://md.research.elamur.ai/free/en/papers/ray-2022-multimodal-corpus-emotion-recognition-sarcasm/)
124. [**A Corpus of Preposition Supersenses**Nathan Schneider, Jena D. Hwang, Vivek Srikumar, et al.*2016 · 34 citations*](https://md.research.elamur.ai/free/en/papers/schneider-2016-corpus-preposition-supersenses/)
125. [**At the interface of syntax and prosody: Differentiating left dislocated and tripartite verbless clauses in Biblical Hebrew**Jacobus A. Naudé, Cynthia L. Miller-Naudé*2017 · 32 citations*](https://md.research.elamur.ai/free/en/papers/naude-2018-interface-syntax-prosody-differentiating-left/)

[← prev](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) [1](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) **2** [3](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/page/3/) [4](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/page/4/) [5](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/page/5/) [next →](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/page/3/)