Enhanced Review Detection and Recognition: A Platform-Agnostic Approach with Application to Online Commerce

TL;DR

This paper introduces a versatile machine learning platform for detecting, extracting, and analyzing online reviews across different websites and languages, enhancing review authenticity and reliability in e-commerce.

Contribution

The authors develop a generalizable review detection and extraction method applicable across websites and languages, with integrated applications for sentiment analysis, multilingual review processing, and fake review identification.

Findings

Effective review detection across unseen websites

Successful multilingual review extraction and translation

Accurate fake review identification using NLP models

Abstract

Online commerce relies heavily on user generated reviews to provide unbiased information about products that they have not physically seen. The importance of reviews has attracted multiple exploitative online behaviours and requires methods for monitoring and detecting reviews. We present a machine learning methodology for review detection and extraction, and demonstrate that it generalises for use across websites that were not contained in the training data. This method promises to drive applications for automatic detection and evaluation of reviews, regardless of their source. Furthermore, we showcase the versatility of our method by implementing and discussing three key applications for analysing reviews: Sentiment Inconsistency Analysis, which detects and filters out unreliable reviews based on inconsistencies between ratings and comments; Multi-language support, enabling the extraction and translation of reviews from various languages without relying on HTML scraping; and Fake review detection, achieved by integrating a trained NLP model to identify and distinguish between genuine and fake reviews.