  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.
