Aspect detection in sportswear apparel reviews for opinion mining

Abstract

Manufacturers and brand owners apply sentiment analysis techniques on customer reviews to identify customer opinions on their products and services. Sentiment analysis at the document level or sentence level does not provide a complete view of the customer opinion because customers may express their opinion on different aspects of the product or service within a single review. This issue has inspired aspect-level opinion mining. Two core tasks are involved with aspect-level opinion mining: aspect detection and aspect-based sentiment analysis. This research is aimed at the first task - aspect detection. The focused domain is sportswear apparel, which has been largely overlooked in the field of opinion mining. Accordingly, this paper presents a new dataset produced with manual annotations by domain experts, according to a newly defined aspect taxonomy. This research compares the performance of a set of pre-trained language models for the considered task, and achieves state-of the-art performance for sportswear apparel reviews using a novel ensemble method.