TipScreener: A Framework for Mining Tips for Online Review Readers

Abstract

User-generated content explodes in popularity daily on e-commerce platforms. It is crucial for platform manipulators to sort out online reviews with repeatedly expressed opinions and a large number of irrelevant topics in order to reduce the information processing burden on review readers. This study proposes a framework named TipScreener that generates a set of useful sentences that cover all of the information of features of a business. Called tips in this work, the sentences are selected from the reviews in their original, unaltered form. Firstly, we identify information tokens of the business. Second, we filter review sentences that contain no tokens and remove duplicates. We then use a convolutional neural network to filter uninformative sentences. Next, we find the tip set with the smallest cardinality that contains all off the tokens, taking opinion words into account. The sentences of the tip set contain a full range of information and have a very low repetition rate. Our work contributes to the work of online review organizing. Review operators of e-commerce platforms can adopt tips generated by TipScreener to facilitate decision makings of review readers. The convolutional neural network that classifies sentences into two classes also enriches deep learning studies on text classification.