Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles
Abstract
Reviews of products or services on Internet marketplace websites contain a rich amount of information. Users often wish to survey reviews or review snippets from the perspective of a certain aspect, which has resulted in a large body of work on aspect identification and extraction from such corpora. In this work, we evaluate a newly-proposed neural model for aspect extraction on two practical tasks. The first is to extract canonical sentences of various aspects from reviews, and is judged by human evaluators against alternatives. A -means baseline does remarkably well in this setting. The second experiment focuses on the suitability of the recovered aspect distributions to represent users by the reviews they have written. Through a set of review reranking experiments, we find that aspect-based profiles can largely capture notions of user preferences, by showing that divergent users generate markedly different review rankings.
Cite
@article{arxiv.1804.08666,
title = {Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles},
author = {Christopher Mitcheltree and Skyler Wharton and Avneesh Saluja},
journal= {arXiv preprint arXiv:1804.08666},
year = {2020}
}
Comments
Equal contribution from first two authors. Accepted for publication in the NAACL 2018 Industry Track