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Reviews of products or services on Internet marketplace websites contain arich amount of information. Users often wish to survey reviews or reviewsnippets from the perspective of a certain aspect, which has resulted in alarge body of work on aspect identification and extraction from such corpora.In this work, we evaluate a newly-proposed neural model for aspect extractionon two practical tasks. The first is to extract canonical sentences of variousaspects from reviews, and is judged by human evaluators against alternatives. A$k$-means baseline does remarkably well in this setting. The second experimentfocuses on the suitability of the recovered aspect distributions to representusers by the reviews they have written. Through a set of review rerankingexperiments, we find that aspect-based profiles can largely capture notions ofuser preferences, by showing that divergent users generate markedly differentreview rankings.

Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles