Crowdsourcing a word–emotion association lexicon SM Mohammad, PD Turney Computational intelligence 29 (3), 436-465, 2013 | 2775 | 2013 |
SemEval-2015 Task 10: Sentiment Analysis in Twitter VS Sara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, Saif M. Mohammad ... arXiv:1912.02387, https://www.aclweb.org/anthology/S15-207, 2015 | 1777* | 2015 |
Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon S Mohammad, P Turney Proceedings of the NAACL HLT 2010 workshop on computational approaches to …, 2010 | 1332 | 2010 |
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets SM Mohammad, S Kiritchenko, X Zhu SemEval-2013, 2013 | 1313 | 2013 |
Sentiment analysis of short informal texts S Kiritchenko, X Zhu, SM Mohammad Journal of Artificial Intelligence Research 50, 723-762, 2014 | 1120 | 2014 |
Semeval-2016 task 6: Detecting stance in tweets S Mohammad, S Kiritchenko, P Sobhani, X Zhu, C Cherry Proceedings of the 10th international workshop on semantic evaluation …, 2016 | 983 | 2016 |
Semeval-2018 task 1: Affect in tweets S Mohammad, F Bravo-Marquez, M Salameh, S Kiritchenko Proceedings of the 12th international workshop on semantic evaluation, 1-17, 2018 | 845 | 2018 |
NRC-Canada-2014: Detecting aspects and sentiment in customer reviews S Kiritchenko, X Zhu, C Cherry, S Mohammad Proceedings of the 8th international workshop on semantic evaluation …, 2014 | 839 | 2014 |
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 828 | 2022 |
Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 English words S Mohammad Proceedings of the 56th annual meeting of the association for computational …, 2018 | 641 | 2018 |
Sentiment analysis: Detecting valence, emotions, and other affectual states from text SM Mohammad Emotion measurement, 201-237, 2016 | 564 | 2016 |
Stance and sentiment in tweets SM Mohammad, P Sobhani, S Kiritchenko ACM Transactions on Internet Technology (TOIT) 17 (3), 1-23, 2017 | 558 | 2017 |
Using hashtags to capture fine emotion categories from tweets SM Mohammad, S Kiritchenko Computational Intelligence 31 (2), 301-326, 2015 | 495 | 2015 |
Examining gender and race bias in two hundred sentiment analysis systems S Kiritchenko, SM Mohammad arXiv preprint arXiv:1805.04508, 2018 | 474 | 2018 |
NRC Emotion Lexicon SM Mohammad, PD Turney National Research Council, Canada 2, 2013 | 443 | 2013 |
From once upon a time to happily ever after: Tracking emotions in mail and books SM Mohammad Decision Support Systems 53 (4), 730-741, 2012 | 440 | 2012 |
Word affect intensities SM Mohammad arXiv preprint arXiv:1704.08798, 2017 | 417 | 2017 |
Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus S Mohammad, C Dunne, B Dorr Proceedings of the 2009 conference on empirical methods in natural language …, 2009 | 328 | 2009 |
#Emotional Tweets SM Mohammad StarSem, 2012 | 319 | 2012 |
WASSA-2017 shared task on emotion intensity SM Mohammad, F Bravo-Marquez arXiv preprint arXiv:1708.03700, 2017 | 305 | 2017 |