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arXiv · 1804.08666

Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles

Christopher Mitcheltree·Skyler Wharton·Avneesh Saluja
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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 $k$-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.

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Christopher Mitcheltree, Skyler Wharton, Avneesh Saluja. 2020-06-04. Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles. https://arxiv.org/abs/1804.08666

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