Emotion-aware polarity lexicons for Twitter sentiment analysis.
Bandhakavi, Anil; Wiratunga, Nirmalie; Massie, Stewart; P., Deepak
Professor Nirmalie Wiratunga email@example.com
Associate Dean for Research
Dr Stewart Massie firstname.lastname@example.org
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.
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|
|Peer Reviewed||Peer Reviewed|
|Keywords||Mapping; Computational emotion detection; Social media; Sentiment; Lexicons|
BANDHAKAVI 2021 Emotion-aware polarity lexicons
This is the peer reviewed version of the following article: BANDHAKAVI, A., WIRATUNGA, N., MASSIE, S. and P, D. 2021. Emotion-aware polarity lexicons for Twitter sentiment analysis. Expert systems, 38(7): artificial intelligence/EDMA 2017, article e12332, which has been published in final form at https://doi.org/10.1111/exsy.12332. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
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