Emotion-aware polarity lexicons for Twitter sentiment analysis.

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

Abstract

Theoretical frameworks in psychology map the relationships between emotions and sentiments. In this paper we study the role of such mapping for computational emotion detection from text (e.g. social media) with a aim to understand the usefulness of an emotion-rich corpus of documents (e.g. tweets) to learn polarity lexicons for sentiment analysis. We propose two different methods that leverage a corpus of emotion-labelled tweets to learn word-polarity lexicons. The proposed methods model the emotion corpus using a generative unigram mixture model (UMM), combined with the emotion-sentiment mapping proposed in Psychology for automated generation of word-polarity lexicons that capture emotion-rich vocabulary. We comparatively evaluate the quality of the proposed mixture model in learning emotion-aware sentiment lexicons with those generated using supervised latent dirichlet allocation (sLDA) and word-document frequency (WDF) statistics. Sentiment analysis experiments on benchmark Twitter data sets confirm the quality of our proposed lexicons. Further a comparative analysis with sLDA, WDF based emotion-aware lexicons and standard sentiment lexicons that are agnostic to emotion knowledge suggest that the proposed lexicons lead to a significantly better performance in both sentiment classification and sentiment intensity prediction tasks.

Citation

BANDHAKAVI, A., WIRATUNGA, N., MASSIE, S. and P, D. 2021. Emotion-aware polarity lexicons for Twitter sentiment analysis. Expert systems [online], 38(7): artificial intelligence/EDMA 2017, article e12332. Available from: https://doi.org/10.1111/exsy.12332

• Journal Article Type: Article
• Acceptance Date: Jun 22, 2018
• Online Publication Date: Oct 11, 2018
• Publication Date: Nov 30, 2021
• Deposit Date: Jun 26, 2018
• Publicly Available Date: Oct 12, 2019
• Journal: Expert systems
• Print ISSN: 0266-4720
• Electronic ISSN: 1468-0394
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 38
• Issue: 7
• Article Number: e12332
• DOI: https://doi.org/10.1111/exsy.12332
• Keywords: Mapping; Computational emotion detection; Social media; Sentiment; Lexicons
• Public URL: http://hdl.handle.net/10059/2969