Lexicon based feature extraction for emotion text classification.
Bandhakavi, Anil; Wiratunga, Nirmalie; Padmanabhan, Deepak; Massie, Stewart
Professor Nirmalie Wiratunga firstname.lastname@example.org
Dr Stewart Massie email@example.com
General Purpose Emotion Lexicons (GPELs) that associate words with emotion categories remain a valuable resource for emotion analysis of text. However the static and formal nature of their vocabularies make them inadequate for extracting effective features for document representation, in domains that are inherently dynamic in nature (e.g. Social Media). This calls for lexicons that are not only adaptive to the lexical variations in a domain but also provide finer-grained quantitative estimates to accurately capture word-emotion associations. In this paper we extend prior work on domain specific emotion lexicon (DSEL) generation and apply it for emotion feature extraction. We demonstrate how our generative unigram mixture model (UMM) based DSEL learnt by harnessing labelled (blogs, news headlines and incident reports) and weakly-labelled (tweets) emotion text can be used to extract effective features for emotion classification. Our results confirm that the features derived using the proposed lexicon outperform those from state-of-the-art lexicons learnt using supervised Latent Dirichlet Allocation (sLDA) and Point-Wise Mutual Information (PMI). Further the proposed lexicon features also outperform state-of-the-art features derived using a combination of n-grams, part-of-speech information and sentiment lexicons.
BANDHAKAVI, A., WIRATUNGA, N., DEEPAK, P. and MASSIE, S. 2017. Lexicon based feature extraction for emotion text classification. Pattern recognition letters [online], 93, pages 133-142. Available from: https://doi.org/10.1016/j.patrec.2016.12.009
|Journal Article Type||Article|
|Acceptance Date||Dec 14, 2016|
|Online Publication Date||Dec 15, 2016|
|Publication Date||Jul 1, 2017|
|Deposit Date||Dec 20, 2016|
|Publicly Available Date||Dec 16, 2017|
|Journal||Pattern recognition letters|
|Peer Reviewed||Peer Reviewed|
|Keywords||Emotion classification; Domain specific word emotion lexicons; Feature extraction|
BANDHAKAVI 2017 Lexicon based feature extraction
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