Clustering for Simultaneous Extraction of Aspects and Features from Reviews
Document Type
Conference Proceeding
Publication Date
6-2016
Abstract
This paper presents a clustering approach that simultaneously identifies product features and groups them into aspect categories from online reviews. Unlike prior approaches that first extract features and then group them into categories, the proposed approach combines feature and aspect discovery instead of chaining them. In addition, prior work on feature extraction tends to require seed terms and focus on identifying explicit features, while the proposed approach extracts both explicit and implicit features, and does not require seed terms. We evaluate this approach on reviews from three domains. The results show that it outperforms several state-of-the-art methods on both tasks across all three domains.
Repository Citation
Chen, L.,
Martineau, J.,
Cheng, D.,
& Sheth, A. P.
(2016). Clustering for Simultaneous Extraction of Aspects and Features from Reviews. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 789-799.
https://corescholar.libraries.wright.edu/knoesis/1124
Comments
Presented at the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics, San Diego, CA, June 12-17, 2016.