Emotion-Corpus Guided Lexicons for Sentiment Analysis on Twitter

Conceptual frameworks for emotion to sentiment mapping have been proposed in Psychology research. 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 word-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 (Cambria et al. 28th AAAI Conference on Artificial Intelligence, pp. 1515–1521, 2014) [1] for automated generation of sentiment lexicons. Sentiment analysis experiments on benchmark Twitter data sets confirm the quality 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 sentiment classification and sentiment intensity prediction tasks.