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Lexicon generation for emotion detection from text.

Bandhakavi, Anil; Wiratunga, Nirmalie; Massie, Stewart; Padmanabhan, Deepak

Authors

Anil Bandhakavi

Nirmalie Wiratunga

Stewart Massie

Deepak Padmanabhan



Abstract

General-purpose emotion lexicons (GPELs) that associate words with emotion categories remain a valuable resource for emotion detection. However, the static and formal nature of their vocabularies make them an inadequate resource for detecting emotions in domains that are inherently dynamic in nature. This calls for lexicons that are not only adaptive to the lexical variations in a domain but which also provide finer-grained quantitative estimates to accurately capture word-emotion associations. In this article, the authors demonstrate how to harness labeled emotion text (such as blogs and news headlines) and weakly labeled emotion text (such as tweets) to learn a word-emotion association lexicon by jointly modeling emotionality and neutrality of words using a generative unigram mixture model (UMM). Empirical evaluation confirms that UMM generated emotion language models (topics) have significantly lower perplexity compared to those from state-of-the-art generative models like supervised Latent Dirichlet Allocation (sLDA). Further emotion detection tasks involving word-emotion classification and document-emotion ranking confirm that the UMM lexicon significantly out performs GPELs and also state-of-the-art domain specific lexicons.

Journal Article Type Article
Publication Date Feb 28, 2017
Journal IEEE intelligent systems
Print ISSN 1541-1672
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 32
Issue 1
Pages 102-108
Institution Citation BANDHAKAVI, A., WIRATUNGA, N., MASSIE, S. and PADMANABHAN, D. 2017. Lexicon generation for emotion detection from text. IEEE intelligent systems [online], 32(1), pages 102-108. Available from: https://doi.org/10.1109/MIS.2017.22
DOI https://doi.org/10.1109/MIS.2017.22
Keywords Intelligent systems; Emotion detection; Domain specific lexicon; Mixture model; Word classification; Emotion ranking

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