Automated genre-based multi-domain sentiment lexicon adaptation using unlabeled data

Abstract

Sentiment analysis research has evolved over the years to extract relevant information from opinionated raw text. Sentiment lexicon is a compiled list of sentiment words and a core component of sentiment analysis tasks. These words play a key role in domain adaptation. Domain adaptation is challenging due to variation in sentiments across the domains. We propose a solution to this research problem by presenting a genre-level sentiment lexicon adaptation approach. The model uses a language domain sense to represent the genre pertaining to the distinct characteristics of the communicated text. The approach addresses the generalization of knowledge at the genre level by learning the multi-source domain lexicon for the selected source domains. The novelty of our approach lies in the genre level relevancy of the source lexicon to the target domains. The model uses unlabeled training data for the source and target domain sentiment lexicon learning. The lexicon adaptation is demonstrated on a long list of target domains that address the three domain adaptation challenges. Experimental results have proved that the model learns the relevant scores and polarities of sentiment words, in addition, it identifies new domain-based sentiment words. The model is evaluated in comparison with standard baselines.