Anil Bandhakavi
Emotion-corpus guided lexicons for sentiment analysis on Twitter.
Bandhakavi, Anil; Wiratunga, Nirmalie; Massie, Stewart; Deepak, P.
Authors
Professor Nirmalie Wiratunga n.wiratunga@rgu.ac.uk
Associate Dean for Research
Dr Stewart Massie s.massie@rgu.ac.uk
Associate Professor
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.
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
Conference Name | 36th SGAI International conference on innovative techniques and applications of artificial intelligence (AI-2016) |
---|---|
Conference Location | Cambridge, UK |
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 |
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 |
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Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
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