Journals Proceedings

Abstract

Aspect-based Sentiment Analysis (ABSA) explores the strong and weak aspects of a product. There are many online platforms that allow users to review commercial products while others to aggregate those opinions across millions of reviews at the aspect level. Such analysis is of high regard to potential customers and manufacturers to make profitable decisions. However, the existing ABSA models do not highlight the reasons behind the strengths and weaknesses of the aspects. Moving a step forward, opinion reason mining explores the reasons for the aspects being appreciated or criticized. We propose opinion reason mining framework ORMFW that uses topic model to generate aspects as groups of aspect terms which are refined using paradigmatic word associations. Polarity is evaluated for each aspect using a dictionary based approach. Furthermore, it incorporates syntagmatic word associations to map the aspects to their respective reason terms against a sentiment polarity. Results on twitter dataset reveal that the proposed ORMFW framework efficiently and effectively identifies the prominent opinion reasons in relation to their aspects