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Emotion-corpus guided lexicons for sentiment analysis on Twitter.

Bandhakavi, Anil; Wiratunga, Nirmalie; Massie, Stewart; Deepak, P.

Authors

Anil Bandhakavi

Nirmalie Wiratunga

Stewart Massie

P. Deepak



Contributors

Max Bramer
Editor

Miltos Petridis
Editor

Abstract

Research in Psychology have proposed frameworks that map emotion concepts with sentiment concepts. In this paper we study this mapping from a computational modelling perspective with a view to establish the role of an emotion-rich corpus for lexicon-based sentiment analysis. We propose two different methods which harness an emotion-labelled corpus of tweets to learn world-level numerical quantification of sentiment strengths over a positive to negative spectrum. The proposed methods model the emotion corpus using a generative unigram mixture model (UMM), combined with the emotion-sentiment mapping proposed in Psychology [6] for automated generation of sentiment lexicons. Sentiment analsysis experiments on benchmark Twitter data sets confirm the equality of our proposed lexicons. Further a comparative analysis with standard sentiment lexicons suggest that the proposed lexicons lead to a significantly better performance in both sentimentclassification and sentiment intensity prediction tasks.

Start Date Dec 13, 2016
Publication Date Nov 5, 2016
Publisher Springer (part of Springer Nature)
Pages 71-85
ISBN 9783319471747
Institution Citation BANDHAKAVI, A., WIRATUNGA, N. and MASSIE, S. 2016. Emotion-corpus guided lexicons for sentiment analysis on Twitter. In Bramer, M. and Petridis, M. (eds.) 2016. Research and development in intelligent systems XXXIII: incorporating applications and innovations in intelligent systems XXIV: proceedings of the 36th SGAI nternational conference on innovative techniques and applications of artificial intelligence (SGAI 2016), 13-15 December 2016, Cambridge, UK. Cham: Springer [online], pages 71-86. Available from: https://doi.org/10.10007/978-3-319-47175-4_5
DOI https://doi.org/10.1007/978-3-319-47175-4_5
Keywords Knowledge discoverty; Data mining; Speech; Natural language interfaces; Machine learning; Ontologies semantic web

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