Emotion-aware polarity lexicons for Twitter sentiment analysis

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

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 an 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, 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 suggests that the proposed lexicons lead to a significantly better performance in both sentiment classification and sentiment intensity prediction tasks.

Original languageEnglish
Article numbere12332
JournalExpert Systems
Early online date11 Oct 2018
DOIs
Publication statusEarly online date - 11 Oct 2018

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Emotion-aware polarity lexicons for Twitter sentiment analysis'. Together they form a unique fingerprint.

  • Cite this