Portable features for classifying emotional text

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TypeArticle
Proceedings titleProceedings of The 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL:HLT-2012), Montreal, QC, June 3-8, 2012
ConferenceThe 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL:HLT-2012), Montreal, Canada, June 3-8, 2012
Pages587591; # of pages: 5
AbstractAre word-level affect lexicons useful in detecting emotions at sentence level? Some prior research finds no gain over and above what is obtained with ngram features-arguably the most widely used features in text classification. Here, we experiment with two very different emotion lexicons and show that even in supervised settings, an affect lexicon can provide significant gains. We further show that while ngram features tend to be accurate, they are often unsuitable for use in new domains. On the other hand, affect lexicon features tend to generalize and produce better results than ngrams when applied to a new domain.
Publication date
PublisherAssociation for Computational Linguistics
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada
Peer reviewedYes
NPARC number20262878
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Record identifier5e92e488-20af-489f-ae82-c55c0f4fa395
Record created2012-07-10
Record modified2016-05-09
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