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 language | English |
---|---|
Title of host publication | International Conference on Recent Advances in Natural Language Processing: Proceedings |
Pages | 959–967 |
Publication status | Published - 01 Sept 2021 |
Publication series
Name | RANLP : Recent Advances in Natural Language Processing: Proceedings |
---|---|
ISSN (Print) | 2603-2813 |
ISSN (Electronic) | 1313-8502 |
Fingerprint
Dive into the research topics of 'Ranking Online Reviews Based on Their Helpfulness: An Unsupervised Approach'. Together they form a unique fingerprint.Student theses
-
Intelligent methods for analyzing veracity and helpfulness of online reviews
Melleng, A. (Author), Padmanabhan, D. (Supervisor) & Jurek-Loughrey, A. (Supervisor), Dec 2024Student thesis: Doctoral Thesis › Doctor of Philosophy
File