Roles and responsibilities
Dr. Saif M. Mohammad is a Senior Research Scientist at the National Research Council Canada (NRC). He received his Ph.D. in Computer Science from the University of Toronto. Before joining NRC, Saif was a Research Associate at the Institute of Advanced Computer Studies at the University of Maryland, College Park. His research interests are in Computational Linguistics and Natural Language Processing (NLP), especially Lexical Semantics, Emotions in Language, Sentiment Analysis, Computational Creativity, Fairness in NLP, Psycholinguistics, and Information Visualization. He has published over 80 scientific articles and his work is widely cited (about 1500 citations in 2018 alone). He has served in various capacities at prominent journals and conferences, including: chair of the Canada--UK symposium on Ethics in AI, co-chair of SemEval (the largest platform for semantic evaluations), co-organizer of WASSA (a sentiment analysis workshop), and area chair for ACL, NAACL, and EMNLP (in the areas of sentiment analysis and fairness in NLP). His team developed a sentiment analysis system which ranked first in shared task competitions. His word-emotion resources, such as the NRC Emotion Lexicon, are widely used for for analyzing affect in text. His work has garnered media attention, including articles in Time, SlashDot, LiveScience, io9, The Physics arXiv Blog, PC World, and Popular Science.
You can watch a video of the talk at the Alan Turing Institute (London, March 2019) summarizing recent work.
Click here for Saif M. Mohammad's Research Home Page.
- Area chair for Sentiment Analysis and Argumentation Mining, EMNLP-IJCNLP-2019, Hong Kong
- Area chair for Sentiment Analysis and Argumentation Mining, ACL-2019, Florence, Italy
- Area chair for Ethics, Bias and Fairness, NAACL-2019, Minneapolis, Minnesota, USA
- Area chair for Semantics in NLP Applications, *Sem-2019, Minneapolis, Minnesota, USA
- Chair of the 2019 Canada–UK Symposium on Ethics in AI, Ottawa, Canada
- Co-chair of SemEval 2019, Minneapolis, Minnesota, USA
- Mentor, Student Research Workshop, ACL-2019, Florence, Italy
- Mentor, Student Research Workshop, NAACL-2019, Minneapolis, Minnesota, USA
- Co-chair of SemEval-2018, New Orleans, Louisiana, USA
- Co-organizer of WASSA-2018, Brussels, Belgium
- Mentor, Student Research Workshop, NAACL-2018, New Orleans, Louisiana, USA
- Mentor, Student Research Workshop, ACL-2018, Melbourne, Australia.
- Co-chair of SemEval-2017, Vancouver, Canada
- Co-organizer of WASSA-2018, Copenhagen, Denmark
- Area chair for Sentiment and Opinion Mining, ACL-2017, Vancouver, Canada
- Area chair for Sentiment and Opinion Mining, EMNLP-2017, Copenhagen, Denmark
- Mentor, Student Research Workshop, ACL-2017, Vancouver, Canada.
- Area chair for Sentiment and Opinion Mining, EMNLP-2016, Austin, Texas, USA
- Publicity chair, EMNLP-2016, Austin, Texas, USA
- Area chair for Sentiment and Opinion Mining, NAACL-2015, Denver, Colorado
- Publicity chair, NAACL-2015, Denver, Colorado
- Mentor, Student Research Workshop, NAACL-2015, Denver, Colorado
2014, 2012, 2011
- Mentor, Student Research Workshops, ACL-2014, NAACL-2012, ACL-2011
Most years 2008–present
- Program committee member for top NLP journals and conferences, including TACL, ACL, NAACL, EMNLP, CL, CI, NLE, JAIR, TSLP, IJCNLP, ICWSM, and IJCAI.
Submissions to International Shared Task Competitions
- AMIA Shared Task on detecting adverse drug reactions in tweets,Washington, DC, USA, 2017
Task: Classification of tweets mentioning adverse drug reactions
Result: 1st place (9 teams participated)
- SemEval-2014 Task 4: Aspect Based Sentiment Analysis, August 2014, Dublin, Ireland
Task: Determine sentiment towards aspect terms and aspect categories
Result: 1st place in two of the three sentiment tasks (30+ teams participated)
- SemEval-2014 Task 9: Sentiment Analysis in Twitter, August 2014, Dublin, Ireland
Task: Determine sentiment of tweets, SMS messages, and blog posts
Result: 1st place in five of ten sub-tasks (40+ teams participated)
- SemEval-2013 Task 2: Sentiment Analysis in Twitter, June 2013, Atlanta, USA
Task: Determine sentiment of tweets and SMS messages
Result: 1st place in three of four sub-tasks (40+ teams participated)
Inventions and patents
Created several datasets and lexicons that are widely used both in computer science and other fields such as psychology, humanities, and public health.
- Lexcions, including:
The full list of lexicons is available here.
- Labeled Datasets, including:
The full list of datasets is available here.
- The NRC-Canada Sentiment Analysis System: ranked first in three sentiment shared tasks: SemEval-2013 Task 2 (Mohammad, Kiritchenko, and Zhu, 2013), SemEval-2014 Task 9 (Zhu, Kiritchenko, and Mohammad, 2014), and SemEval- 2014 Task 4 (Kiritchenko, Zhu, and Mohammad, 2014). Many of the same features used in NRC-Canada were also used in a stance-detection system that outperformed submissions from all 19 teams that participated in SemEval-2016 Task 6 (Mohammad et al., 2017).
Information visualization and Data sonification demos
- TransProse: Converting Text to Music. Hannah Davis and Saif M. Mohammad.
A system that takes as input classic English novels and generates music that captures the flow of emotions in it.
- ImagiSaurus: An Interactive Visualization of the Roget’s Thesaurus
Imagisaurus helps users quickly grasp the nature and size of the thesaurus taxonomy. Addition- ally, Imagisaurus allows exploration of affectual categories in the thesaurus.
- Lexichrome: An Interactive Word–Color Catalogue for Scholars, Designers, and Writers. Chris Kim, Saif M. Mohammad, and Christopher Colins.
Lexichrome is an application that maps the NRC Word–Colour Association Lexicon to a web- based catalogue that users can browse and analyze. It allows visitors to view the chromatic makeup (color associations) of a user-provided text.
- An Interactive Visualizer for the NRC Emotion Lexicon
The NRC Emotion Lexicon is a list of English words and their associations with eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive). The annotations were manually done by crowdsourcing.
- Interactive Visualizers for Sentiment Composition Lexicons
Sentiment composition is the determining of sentiment of a multi-word linguistic unit, such as a phrase or a sentence, based on its constituents. We present two visualizations: (1) exploring sentiment composition in phrases formed by at least one positive and at least one negative word- phrases like happy accident and best winter break, and (2) exploring sentiment composition in phrases formed with negators, modals, and degree adverbs.
- An Interactive Visualizer for the Stance Dataset
We visualize a dataset of tweets manually annotated for stance towards given target, target of opinion (opinion towards), and sentiment (polarity).
A full list of publications can be found at the Google Scholar page. Below are some notable publications:
- The Natural Selection of Words: Finding the Features of Fitness. Peter D. Turney and Saif M. Mohammad. PLoS One, 14 (1):e0211512. January 2019.
- Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words. Saif M. Mohammad. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, July 2018.
- Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems. Svetlana Kiritchenko and Saif M. Mohammad. In Proceedings of *Sem, New Orleans, LA, USA, June 2018.
- WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art. Saif M. Mohammad and Svetlana Kiritchenko. In Proceedings of the 11th Edition of the Language Resources and Evaluation Conference (LREC-2018), May 2018, Miyazaki, Japan.
- Crowdsourcing a Word-Emotion Association Lexicon, Saif Mohammad and Peter Turney, Computational Intelligence, 29 (3), 436-465, 2013.
- Using Nuances of Emotion to Identify Personality, Saif M. Mohammad and Svetlana Kiritchenko, In Proceedings of the ICWSM Workshop on Computational Personality Recognition, July 2013, Boston, USA.
- From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales, Saif Mohammad, In Proceedings of the ACL 2011 Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH), June 2011, Portland, OR.
- Stance and Sentiment in Tweets. Saif M. Mohammad, Parinaz Sobhani, and Svetlana Kiritchenko. Special Section of the ACM Transactions on Internet Technology on Argumentation in Social Media, 2017, 17(3).
- Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text. Saif M. Mohammad, Emotion Measurement, 2016.
- How Translation Alters Sentiment. Saif M. Mohammad, Mohammad Salameh, and Svetlana Kiritchenko, Journal of Artificial Intelligence Research, 2016, Volume 55, pages 95-130.
- Sentiment Analysis of Short Informal Texts. Svetlana Kiritchenko, Xiaodan Zhu and Saif Mohammad. Journal of Artificial Intelligence Research, volume 50, pages 723-762, August 2014.
- Computing Word-Pair Antonymy, Saif Mohammad, Bonnie Dorr, and Graeme Hirst, In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-2008), October 2008, Waikiki, Hawaii.
Previous work experience
Before joining NRC, Saif was a Research Associate at the Institute of Advanced Computer Studies at the University of Maryland, College Park.