Ranking Online Reviews Based on Their Helpfulness: An Unsupervised Approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)
74 Downloads (Pure)

Abstract

Online reviews are an essential aspect of on-line shopping for both customers and retailers. However, many reviews found on the Internet lack in quality, informativeness or help-fulness. In many cases, they lead the customers towards positive or negative opinions without providing any concrete details (e.g.,very poor product, I would not recommend it). In this work, we propose a novel unsupervised method for quantifying helpfulness leveraging the availability of a corpus of reviews.In particular, our method exploits three characteristics of the reviews, viz., relevance, emotional intensity and specificity, towards quantifying helpfulness. We perform three rankings(one for each feature above), which are then combined to obtain a final helpfulness ranking.For the purpose of empirically evaluating our method, we use review of four product categories from Amazon review1. The experimental evaluation demonstrates the effectiveness of our method in comparison to a recent and state-of-the-art baseline.
Original languageEnglish
Title of host publicationInternational Conference on Recent Advances in Natural Language Processing: Proceedings
Pages959–967
Publication statusPublished - 01 Sept 2021

Publication series

NameRANLP : Recent Advances in Natural Language Processing: Proceedings
ISSN (Print)2603-2813
ISSN (Electronic)1313-8502

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