Discovering aspects of online consumer reviews

In this paper we propose a fully unsupervised approach for product aspect discovery in on-line consumer reviews. We apply a two-step hierarchical clustering process in which we first cluster words representing aspects based on the semantic similarity of their contexts and then on the similarity of t...

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Published in:Journal of information science 2016-08, Vol.42 (4), p.492-506
Main Authors: Suleman, Kaheer, Vechtomova, Olga
Format: Article
Language:English
Subjects:
Cluster analysis
Clustering
Clusters
Consumers
Datasets
Product reviews
Semantics
Similarity
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description In this paper we propose a fully unsupervised approach for product aspect discovery in on-line consumer reviews. We apply a two-step hierarchical clustering process in which we first cluster words representing aspects based on the semantic similarity of their contexts and then on the similarity of the hypernyms of the cluster members. Our approach also includes a method for assigning class labels to each of the clusters. We evaluated our methods on large datasets of restaurant and camera reviews and found that the two-step clustering process performed better than a single-step clustering process at identifying aspects and words refering to aspects. Finally, we compare our method to a state-of-the-art topic modelling approach by Titov and McDonald, and demonstrate better results on both datasets.
doi_str_mv 10.1177/0165551515595742
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source Library & Information Science Abstracts (LISA); SAGE:Jisc Collections:SAGE Journals Read and Publish 2023-2024:2025 extension (reading list)
subjects Cluster analysis
Clustering
Clusters
Consumers
Datasets
Product reviews
Semantics
Similarity
title Discovering aspects of online consumer reviews
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