Web Item Reviewing Made Easy By Leveraging Available User Feedback
Azade Nazi, Mahashweta Das, Gautam Das

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- 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsConsumer Market Behavior and Pricing · Recommender Systems and Techniques · Web Data Mining and Analysis
