Web Item Reviewing Made Easy By Leveraging Available User Feedback

TL;DR

This paper introduces a framework for extracting meaningful, relevant, and sentiment-aware tags from user reviews to facilitate easier and more comprehensive online item reviews, especially when detailed reviews are scarce.

Contribution

It proposes a novel constrained optimization framework with practical algorithms for identifying top-k meaningful tags based on relevance, coverage, and polarity, addressing review sparsity.

Findings

Effective tag extraction validated on real web data

Algorithms with theoretical bounds for efficiency

Improved review summarization and user assistance

Abstract

The widespread use of online review sites over the past decade has motivated businesses of all types to possess an expansive arsenal of user feedback to mark their reputation. Though a significant proportion of purchasing decisions are driven by average rating, detailed reviews are critical for activities like buying expensive digital SLR camera. Since writing a detailed review for an item is usually time-consuming, the number of reviews available in the Web is far from many. Given a user and an item our goal is to identify the top-$k$ meaningful phrases/tags to help her review the item easily. We propose general-constrained optimization framework based on three measures - relevance (how well the result set of tags describes an item), coverage (how well the result set of tags covers the different aspects of an item), and polarity (how well sentiment is attached to the result set of tags). By adopting different definitions of coverage, we identify two concrete problem instances that enable a wide range of real-world scenarios. We develop practical algorithms with theoretical bounds to solve these problems efficiently. We conduct experiments on synthetic and real data crawled from the web to validate the effectiveness of our solutions.