Aspect Extraction from Product Reviews Using Category Hierarchy Information

Yinfei Yang, Cen Chen, Minghui Qiu, Forrest Bao


Abstract
Aspect extraction abstracts the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 categories of Amazon.com products show that both common aspects of parent category and the individual aspects of sub-categories can be extracted to align well with the common sense. We further evaluate the manually extracted aspects of 16 products, resulting in an average hit rate of 79.10%.
Anthology ID:
E17-2107
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
675–680
Language:
URL:
https://aclanthology.org/E17-2107
DOI:
Bibkey:
Cite (ACL):
Yinfei Yang, Cen Chen, Minghui Qiu, and Forrest Bao. 2017. Aspect Extraction from Product Reviews Using Category Hierarchy Information. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 675–680, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Aspect Extraction from Product Reviews Using Category Hierarchy Information (Yang et al., EACL 2017)
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PDF:
https://aclanthology.org/E17-2107.pdf