Accessing the NRC Emotion and Sentiment Lexicons


Since February 16, 2017 the lexicons listed below are no longer available for direct download at this website. They are still available free of charge for research purposes. They are also available for commercial applications via a perpetual commercial license for a nominal one-time fee.

Simply email:

Saif M. Mohammad (Senior Research Officer at NRC and creator of these lexicons): Saif.Mohammad@nrc-cnrc.gc.ca.
and
Pierre Charron (Client Relationship Leader at NRC): Pierre.Charron@nrc-cnrc.gc.ca

and include:

- your name and affiliation
- the name of the resource you are interested in
- what you intend to use it for
- whether you intend to use it only for research and will not include it in any commercial product OR you would like a commercial license

Pierre Charron will get back to you with a link to download, terms of use, and licensing information.
Technical and research-related questions can be addressed just to Saif M. Mohammad: Saif.Mohammad@nrc-cnrc.gc.ca.

We usually respond by the next business day, but if you have not heard back from us for a few days, please email us again. We get a number of requests every day, and on rare occasions an email can be unintentionally overlooked.

Terms of Use:

The Sentiment and Emotion Lexicons you can obtain by emailing include:

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 for 1:

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 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 for 2:

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

3. 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

4. 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

5. 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

6. 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 3, 4, 5, and 6:

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

7. 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 for 7:

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