Generating a Word-Emotion Lexicon from #Emotional Tweets

Anil Bandhakavi, Nirmalie Wiratunga, Deepak Padmanabhan, Stewart Massie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)
2205 Downloads (Pure)


Research in emotion analysis of text suggest that emotion lexicon based features are superior to corpus based n-gram features. However the static nature of the general purpose emotion lexicons make them less suited to social media analysis, where the need to adopt to changes in vocabulary usage and context is crucial. In this paper we propose a set of methods to extract a word-emotion lexicon automatically from an emotion labelled corpus of tweets. Our results confirm that the features derived from these lexicons outperform the standard Bag-of-words features when applied to an emotion classification task. Furthermore, a comparative analysis with both manually crafted lexicons and a state-of-the-art lexicon generated using Point-Wise Mutual Information, show that the lexicons generated from the proposed methods lead to significantly better classi- fication performance.
Original languageEnglish
Title of host publicationProceedings of the Third Joint Conference on Lexical and Computational Semantics (*SEM 2014)
Publisher*SEM 2014 Organising Committee
Number of pages10
ISBN (Print) 9781941643259
Publication statusPublished - 2014
Event*SEM 2014 Third Joint Conference on Lexical and Computational Semantics - Ireland, Dublin, Ireland
Duration: 23 Aug 201424 Aug 2014


Conference*SEM 2014 Third Joint Conference on Lexical and Computational Semantics


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