Confirmatory Aspect-based Opinion Mining Processes

TL;DR

This paper introduces a confirmatory aspect-based opinion mining framework and algorithm, DiSSBUS, to extract and summarize opinions from customer reviews by decomposing reviews into topic-evaluation bi-terms, improving topic relevance and sentiment detection.

Contribution

The paper presents a novel confirmatory opinion mining approach with a practical algorithm that decomposes reviews into topic-related bi-terms, addressing sparsity and sentiment score issues in existing methods.

Findings

Effective decomposition of reviews into topic-evaluation bi-terms

Validated on restaurant reviews from TripAdvisor in Hawaii

Demonstrated improved opinion extraction accuracy

Abstract

A new opinion extraction method is proposed to summarize unstructured, user-generated content (i.e., online customer reviews) in the fixed topic domains. To differentiate the current approach from other opinion extraction approaches, which are often exposed to a sparsity problem and lack of sentiment scores, a confirmatory aspect-based opinion mining framework is introduced along with its practical algorithm called DiSSBUS. In this procedure, 1) each customer review is disintegrated into a set of clauses; 2) each clause is summarized to bi-terms-a topic word and an evaluation word-using a part-of-speech (POS) tagger; and 3) each bi-term is matched to a pre-specified topic relevant to a specific domain. The proposed processes have two primary advantages over existing methods: 1) they can decompose a single review into a set of bi-terms related to pre-specified topics in the domain of interest and, therefore, 2) allow identification of the reviewer's opinions on the topics via evaluation words within the set of bi-terms. The proposed aspect-based opinion mining is applied to customer reviews of restaurants in Hawaii obtained from TripAdvisor, and the empirical findings validate the effectiveness of the method. Keywords: Clause-based sentiment analysis, Customer review, Opinion mining, Topic modeling, User-generate-contents.