Opinion Reason Mining: Implicit Aspects beyond Implying aspects

Abstract

Aspect-based Sentiment Analysis (ABSA) aggregates the user opinions at the aspects level. Therefore, it offers a detailed analysis of the product by highlighting its strong and weak aspects. Potential customers and manufacturers highly regard such analysis to make profitable future decisions. However, the existing models do not provide reasons for an aspect being praised or criticized. Such information may help users to assess if the reasons mentioned by reviewers in support or against an aspect of a product are aligned with their priorities. We propose an approach that weighs implicit aspect terms beyond implying aspects and suggesting their polarity. The proposed approach makes use of linguistic associations to identify prominent implicit aspect terms for an aspect. They are presented as possible reasons for an aspect to attain a polarity score. The results are evaluated on online twitter data which indicate effective exploration of opinion reasons.