Enhanced Review Detection and Recognition: A Platform-Agnostic Approach with Application to Online Commerce
This addresses the need for monitoring exploitative behaviors in online commerce by providing a platform-agnostic tool for review analysis, though it appears incremental as it builds on existing detection methods.
The paper tackles the problem of detecting and extracting user-generated reviews across different online commerce platforms, presenting a machine learning method that generalizes to unseen websites and enables applications like sentiment inconsistency analysis, multi-language support, and fake review detection.
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.