Emotion-corpus guided lexicons for sentiment analysis on Twitter.

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

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.

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

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 36th SGAI International conference on innovative techniques and applications of artificial intelligence (AI-2016)
• Start Date: Dec 13, 2016
• End Date: Dec 15, 2016
• Acceptance Date: Jun 10, 2016
• Online Publication Date: Nov 5, 2016
• Publication Date: Nov 5, 2016
• Deposit Date: Nov 11, 2016
• Publicly Available Date: Nov 6, 2017
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 71-85
• ISBN: 9783319471747
• 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
• Public URL: http://hdl.handle.net/10059/1972
• Contract Date: Nov 11, 2016