These lexicons are created by manual annotation. The lexicons with real-valued scores are created using Best-Worst Scaling, producing fine-grained, yet highly reliable annotation values.
Large Manually Created Emotion and Sentiment Lexicons LexiconVersion
# of Terms Categories Association Scores Method of Creation1a. NRC Word-Emotion Association Lexicon (also called NRC Emotion lexicon or EmoLex). README. Explore the interactive visualization. Homepage of the Lexicon. Also available in over 40 other languages here. The sense-level annotations provided by individual annotators for the eight emotions can also be obtained.
0.92
(2010)
14,182 unigrams (words)sentiments:
negative, positive
emotions:
anger, anticipation, disgust, fear, joy, sadness, surprise, trust
Manual: By crowdsourcing
Domain: General
~25,000 senses
not associated, weakly, moderately, or strongly associatedPapers:
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
1b. NRC Emotion Intensity Lexicon (aka Affect Intensity Lexicon), created using Best-Worst Scaling.
The NRC Emotion Intensity Lexicon is a list of English words and their associations with eight basic emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, trust). Lexicon homepage.
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
2. NRC Valence, Arousal, Dominance Lexicon, created using Best-Worst Scaling.
The NRC Valence, Arousal, Dominance Lexicon is a list of English words and their valence, arousal, and dominance scores. Lexicon homepage.
1
(2018)
~20,000 termsValence
(positive--negative) Arousal
(excited--calm) Dominance (powerful--weak)
Manual: By crowdsourcing
Domain: General
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
Manually Created Sentiment Composition Lexicons
These lexicons include sentiment scores for two- and three-word expressions as well as scores for their constituent words.
LexiconVersion
# of Terms Categories Association Scores Method of Creation1. created using Best-Worst Scaling (aka MaxDiff)
1.0
(Feb. 2016)
~3200 terms sentiments:negative, positive Real-valued score between -1 (most negative) to 1 (most positive)
Manual. By crowdsourcing and using .
Domain: General
Papers:
- The Effect of Negators, Modals, and Degree Adverbs on Sentiment Composition. Svetlana Kiritchenko and Saif M. Mohammad, In Proceedings of the NAACL 2016 Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media (WASSA), June 2014, San Diego, California.
Paper (pdf) BibTeX Presentation- Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling. Svetlana Kiritchenko and Saif M. Mohammad. In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. June 2016. San Diego, CA.
Paper (pdf) BibTeX Presentation- Semeval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases. Svetlana Kiritchenko, Saif M. Mohammad, and Mohammad Salameh. In Proceedings of the International Workshop on Semantic Evaluation (SemEval ’16). June 2016. San Diego, California.
Paper (pdf) BibTeX Presentation Task Website
2. created using Best-Worst Scaling (aka MaxDiff)
1.0
(Feb. 2015)
~1500 terms sentiments:negative, positive Real-valued score between -1 (most negative) to 1 (most positive)
Manual. By crowdsourcing and using .
Domain: Twitter
Paper:
- SemEval-2015 Task 10: Sentiment Analysis in Twitter. Sara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, Saif M Mohammad, Alan Ritter, and Veselin Stoyanov. In Proceedings of the ninth international workshop on Semantic Evaluation Exercises (SemEval-2015), June 2015, Denver, Colorado.
Paper (pdf) BibTeX- 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) BibTeXThis data was used in SemEval-2015 Task 10 (Sentiment Analysis in Twitter), subtask E - Determining strength of association of Twitter terms with positive sentiment (or, degree of prior polarity). Task description, trial data, test data, and other details available .
3. , created using Best-Worst Scaling (aka MaxDiff)
1.0
(Feb. 2016)
~1200 terms sentiments:negative, positive Real-valued score between -1 (most negative) to 1 (most positive)
Manual. By crowdsourcing and using .
Domain: Twitter
Paper:
Large Manually Created Word-Colour Association Lexicon Lexicon
- Sentiment Composition of Words with Opposing Polarities. Svetlana Kiritchenko and Saif M. Mohammad. In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. June 2016. San Diego, CA.
Paper (pdf) BibTeX Poster- Happy Accident: A Sentiment Composition Lexicon for Opposing Polarities Phrases. Svetlana Kiritchenko and Saif M. Mohammad. In Proceedings of the 10th edition of the Language Resources and Evaluation Conference, May 2016, Portoro� (Slovenia).
Paper (pdf) BibTeX Poster- Semeval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases. Svetlana Kiritchenko, Saif M. Mohammad, and Mohammad Salameh. In Proceedings of the International Workshop on Semantic Evaluation (SemEval ’16). June 2016. San Diego, California.
Paper (pdf) BibTeX Presentation Task Website
Version
# of Terms Categories Association Scores Method of Creation1. NRC Word-Colour Association Lexicon
0.92
(2011)
~14,000 wordscolours:
black, blue, brown, green, grey, orange purple, pink, red, white, yellow 0 (not associated) or 1 (associated)
Manual: Crowdsourcing on Mechanical Turk.
Domain: General
~25,000 senses not, weakly, moderately, or strongly associatedPapers:
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 Created Lexicons
These lexicons are automatically extracted from large amounts of text using co-occurrence information. For example, the Hashtag Emotion Lexicon is generated from tweets and the score for a word--emotion pair is a quantification of the word's tendency to co-occur with the emotion-word hashtag. These are usually much larger than manually created lexicons. They have higher coverage, especially of terms often seen in the corpus that the lexicon is extracted from. However, the emotion scores can be less accurate than those in the manually created lexicons above.
Version
# of Terms Categories Association Scores Method of Creation1. NRC Hashtag Emotion Lexicon. The Hashtag Emotion Corpus (aka Twitter Emotion Corpus, or TEC) used to create the lexicon.
0.2
(2013)
16,862 unigrams (words) emotions:anger, anticipation, disgust, fear, joy, sadness, surprise, trust Real-valued score between 0 (not associated) to ∞ (maximally associated)
Automatic: From tweets with emotion word hashtags.
Domain: Twitter
Papers:
Large Automatically Generated Word-Sentiment Association Lexicons LexiconUsing Hashtags to Capture Fine Emotion Categories from Tweets. Saif M. Mohammad, Svetlana Kiritchenko, Computational Intelligence, Volume 31, Issue 2, Pages 301-326, May 2015. 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
Version
# of Terms Categories Association Scores Method of Creation 1. NRC Twitter Sentiment Lexicons (NRC Hashtag Sentiment Lexicons and Sentiment140 Lexicons)a. NRC Hashtag Sentiment Lexicon
1.0
(2013)
54,129 unigrams sentiments:negative, positive Real-valued score between -∞ (most negative) to ∞ (most positive)
Automatic: From tweets with sentiment word hashtags.
Domain: Twitter
316,531 bigrams 308,808 pairsb. NRC Hashtag Affirmative Context Sentiment Lexicon and NRC Hashtag Negated Context Sentiment Lexicon
1.0
(2014)
Affirmative contexts: 36,357 unigramsNegated contexts: 7,592 unigrams sentiments:
negative, positive Real-valued score between -∞ (most negative) to ∞ (most positive)
Automatic: From tweets with sentiment word hashtags. Separate entries for affirmative and negated contexts.
Domain: Twitter
Affirmative contexts: 159,479 bigramsNegated contexts: 23,875 bigrams
c. Emoticon Lexicon aka Sentiment140 Lexicon (note that this is sentiment lexicon drawn from emoticons, and is not an emotion lexicon)
1.0
(2014)
62,468 unigrams sentiments:negative, positive Real-valued score between -∞ (most negative) to ∞ (most positive)
Automatic: From tweets with emoticons.
Domain: Twitter
677,698 bigrams 480,010 pairsd. Sentiment140 Affirmative Context Lexicon and Sentiment140 Negated Context Lexicon
1.0
(2014)
Negated contexts: 9,891 unigrams sentiments:
negative, positive Real-valued score between -∞ (most negative) to ∞ (most positive)
Automatic: From tweets with sentiment word hashtags. Separate entries for affirmative and negated contexts.
Domain: Twitter
Affirmative contexts: 240,076 bigramsNegated contexts: 34,093 bigrams
Papers (describing the four NRC Twitter Lexicons listed above):
2. Yelp and Amazon Sentiment LexiconsSentiment 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) BibTeXNRC-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 SlidesNRC-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) BibTeXThese lexicons were used to generate winning submissions for the sentiment analysis shared tasks of SemEval-2013 Task 2 and SemEval-2014 Task 9.
a. Yelp Restaurant Sentiment Lexicon
(created from the Yelp Dataset -- from the subset of entries pertaining to these restaurant-related businesses)
1.0
(2014)
39,274 entries for unigrams (includes affirmative and negated context entries) sentiments:negative, positive Real-valued score between -∞ (most negative) to ∞ (most positive)
Automatic: From customer reviews on Yelp.com.
Domain: Restaurant
276,651 entries for bigramsThe Yelp Word–Aspect Association Lexicons are also made available.
b. Amazon Laptop Sentiment Lexicon
1.0
(2014)
26,577 entries for unigrams (includes affirmative and negated context entries) sentiments:negative, positive Real-valued score between -∞ (most negative) to ∞ (most positive)
Automatic: From customer reviews on Amazon.com.
Domain: Laptop
155,167 entries for bigramsPaper (describing the Yelp and Amazon Lexicons):
NRC-Canada-2014: Detecting Aspects and Sentiment in Customer Reviews, Svetlana Kiritchenko, Xiaodan Zhu, Colin Cherry, and Saif M. Mohammad. In Proceedings of the eigth international workshop on Semantic Evaluation Exercises (SemEval-2014), August 2014, Dublin, Ireland. Paper (pdf) BibTeX
These lexicons were used to generate winning submissions for the sentiment analysis shared task of SemEval-2014 Task 4.
3. Macquarie Semantic Orientation Lexicon
0.1
(2009)
76,400 terms sentiments:negative, positive binary distinction: negative or positive
Automatic: Using the structure of a thesaurus and affixes.
Domain: General
Paper:
Generating High-Coverage Semantic Orientation Lexicons From Overtly Marked Words and a Thesaurus, Saif Mohammad, Bonnie Dorr, and Cody Dunne, In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP-2009), August 2009, Singapore. Paper (pdf) BibTeX Presentation
What are the associations and feelings evoked by a word?
Connotation is the emotional and imaginative association surrounding a word.Which of the following is the feeling or association a word or phrase evokes in addition to its literal meaning?
A connotation is a commonly understood cultural or emotional association that any given word or phrase carries, in addition to its explicit or literal meaning, which is its denotation.What is the feeling a word evokes instead of its dictionary definition called?
When someone refers to a word's connotation, they're referring to what it implies or suggests—or to the secondary meanings or implications that are associated with it. The word connotation is commonly used in the phrases positive connotation and negative connotation.What is it called when a word is evoking an emotion or idea on top of its literal meaning with the meaning of the word changing based on the situation or context?
Pathos, or the appeal to emotion, means to persuade an audience by purposely evoking certain emotions to make them feel the way the author wants them to feel. Authors make deliberate word choices, use meaningful language, and use examples and stories that evoke emotion.