Document-Level Multi-Aspect Sentiment Classification as Machine Comprehension

Yichun Yin, Yangqiu Song, Ming Zhang


Abstract
Document-level multi-aspect sentiment classification is an important task for customer relation management. In this paper, we model the task as a machine comprehension problem where pseudo question-answer pairs are constructed by a small number of aspect-related keywords and aspect ratings. A hierarchical iterative attention model is introduced to build aspectspecific representations by frequent and repeated interactions between documents and aspect questions. We adopt a hierarchical architecture to represent both word level and sentence level information, and use the attention operations for aspect questions and documents alternatively with the multiple hop mechanism. Experimental results on the TripAdvisor and BeerAdvocate datasets show that our model outperforms classical baselines. We will release our code and data for the method replicability.
Anthology ID:
D17-1217
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2044–2054
Language:
URL:
https://www.aclweb.org/anthology/D17-1217
DOI:
10.18653/v1/D17-1217
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1217.pdf
Attachment:
 D17-1217.Attachment.pdf
Video:
 https://vimeo.com/238232532