NRC Emotion Lexicon

To access the NRC Emotion and Sentiment Lexicons, select the appropriate license below:

Technical and research-related questions can be addressed to Saif M. Mohammad (Senior Research Scientist at NRC): Saif.Mohammad@nrc-cnrc.gc.ca.

For questions about the commercial license, email Pierre Charron (Client Relationship Leader at NRC): Pierre.Charron@nrc-cnrc.gc.ca

Terms of Use:

Cite the papers associated with the lexicons in your research papers and articles that make use of them. (The papers associated with each lexicon are listed below, and also in the READMEs for individual lexicons.)
In news articles and online posts on work using these lexicons, cite the appropriate lexicons. For example: "This application/product/tool makes use of the <resource name>, created by <author(s)> at the National Research Council Canada." (The creators of each lexicon are listed below. Also, if you send us an email, we will be thrilled to know about how you have used the lexicon.) If possible hyperlink to this page: http://saifmohammad.com/WebPages/lexicons.html
If you use a lexicon in a product or application, then acknowledge this in the 'About' page and other relevant documentation of the application by stating the name of the resource, the authors, and NRC. For example: "This application/product/tool makes use of the <resource name>, created by <author(s)> at the National Research Council Canada." (The creators of each lexicon are listed below. Also, if you send us an email, we will be thrilled to know about how you have used the lexicon.) If possible hyperlink to this page: http://saifmohammad.com/WebPages/lexicons.html
Do not redistribute the data. Direct interested parties to this page:
http://saifmohammad.com/WebPages/AccessResource.htm
National Research Council Canada (NRC) disclaims any responsibility for the use of the lexicons listed here and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.

The Sentiment and Emotion Lexicons include:

Manually Created Lexicons

1. NRC Word-Emotion Association Lexicon aka NRC Emotion Lexicon aka EmoLex: association of words with eight emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive) manually annotated on Amazon's Mechanical Turk. Available in 40 different languages.
Version: 0.92
Number of terms: 14,182 unigrams (words), ~25,000 word senses
Association scores: binary (associated or not)
Creators: Saif M. Mohammad and Peter D. Turney

Papers:

Crowdsourcing a Word-Emotion Association Lexicon, Saif Mohammad and Peter Turney, Computational Intelligence, 29 (3), 436-465, 2013.    Paper (pdf)    BibTeX

Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon, Saif Mohammad and Peter Turney, In Proceedings of the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, June 2010, LA, California.    Paper (pdf)    BibTeX    Presentation

2. NRC Valence, Arousal, Dominance Lexicon: The NRC Valence, Arousal, Dominance Lexicon is a list of English words and their valence, arousal, and dominance scores. Lexicon homepage.

Paper:

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.
Paper (pdf)    BibTeX

3. NRC Affect Intensity Lexicon. The NRC Affect Intensity Lexicon is a list of English words and their associations with four basic emotions (anger, fear, sadness, joy). Lexicon homepage.

Paper:

Word Affect Intensities. Saif M. Mohammad. In Proceedings of the 11th Edition of the Language Resources and Evaluation Conference (LREC-2018), May 2018, Miyazaki, Japan.
Paper (pdf)    BibTeX       Presentation

4. NRC Word-Colour Association Lexicon: association of words with colours manually annotated on Amazon's Mechanical Turk.
Version: 0.92
Number of terms: 14,182 unigrams (words), ~25,000 word senses
Association scores: binary (associated or not)
Creator: Saif M. Mohammad

Papers:

Colourful Language: Measuring Word-Colour Associations, Saif Mohammad, In Proceedings of the ACL 2011 Workshop on Cognitive Modeling and Computational Linguistics (CMCL), June 2011, Portland, OR.    Paper (pdf)    BibTeX     Presentation

Even the Abstract have Colour: Consensus in Word-Colour Associations, Saif Mohammad, In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, June 2011, Portland, OR.    Paper (pdf)    BibTeX     Poster

Automatically Generated Lexicons

1. NRC Hashtag Emotion Lexicon: association of words with eight emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) generated automatically from tweets with emotion-word hashtags such as #happy and #anger.
Version: 0.2
Number of terms: 16,862 unigrams (words)
Association scores: real-valued
Creator: Saif M. Mohammad

Papers:

Using Hashtags to Capture Fine Emotion Categories from Tweets. Saif M. Mohammad, Svetlana Kiritchenko, Computational Intelligence, in press.     Paper (pdf)    BibTeX

#Emotional Tweets, Saif Mohammad, In Proceedings of the First Joint Conference on Lexical and Computational Semantics (*Sem), June 2012, Montreal, Canada.    Paper (pdf)    BibTeX

2. NRC Hashtag Sentiment Lexicon: association of words with positive (negative) sentiment generated automatically from tweets with sentiment-word hashtags such as #amazing and #terrible.
Version: 1.0
Number of terms: 54,129 unigrams, 316,531 bigrams, 308,808 pairs
Association scores: real-valued
Creators: Saif M. Mohammad and Svetlana Kiritchenko

3. NRC Hashtag Affirmative Context Sentiment Lexicon and NRC Hashtag Negated Context Sentiment Lexicon: association of words with positive (negative) sentiment in affirmative or negated contexts generated automatically from tweets with sentiment-word hashtags such as #amazing and #terrible.
Version: 1.0
Number of terms: Affirmative contexts: 36,357 unigrams, 159,479 bigrams; Negated contexts: 7,592 unigrams, 23,875 bigrams
Association scores: real-valued
Creators: Svetlana Kiritchenko and Saif M. Mohammad

4. NRC Emoticon Lexicon (a.k.a. Sentiment140 Lexicon): association of words with positive (negative) sentiment generated automatically from tweets with emoticons such as :) and :(.
Version: 1.0
Number of terms: 62,468 unigrams, 677,698 bigrams, 480,010 pairs
Association scores: real-valued
Creators: Saif M. Mohammad and Svetlana Kiritchenko

5. NRC Emoticon Affirmative Context Lexicon and NRC Emoticon Negated Context Lexicon: association of words with positive (negative) sentiment in affirmative or negated contexts generated automatically from tweets with emoticons such as :) and :(.
Version: 1.0
Number of terms: Affirmative contexts: 45,255 unigrams, 240,076 bigrams; Negated contexts: 9,891 unigrams, 34,093 bigrams
Association scores: real-valued
Creators: Svetlana Kiritchenko and Saif M. Mohammad

Papers for 2, 3, 4, and 5:

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.
Paper (pdf)    BibTeX

NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets, Saif M. Mohammad, Svetlana Kiritchenko, and Xiaodan Zhu, In Proceedings of the seventh international workshop on Semantic Evaluation Exercises (SemEval-2013), June 2013, Atlanta, USA.
Paper (pdf)    BibTeX    System Description and Downloads     Poster     Slides

NRC-Canada-2014: Recent Improvements in Sentiment Analysis of Tweets, Xiaodan Zhu, Svetlana Kiritchenko, and Saif M. Mohammad. In Proceedings of the eigth international workshop on Semantic Evaluation Exercises (SemEval-2014), August 2014, Dublin, Ireland.
Paper (pdf)    BibTeX