Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews

Lahari Poddar, Wynne Hsu, Mong Li Lee


Abstract
User generated content about products and services in the form of reviews are often diverse and even contradictory. This makes it difficult for users to know if an opinion in a review is prevalent or biased. We study the problem of searching for supporting opinions in the context of reviews. We propose a framework called SURF, that first identifies opinions expressed in a review, and then finds similar opinions from other reviews. We design a novel probabilistic graphical model that captures opinions as a combination of aspect, topic and sentiment dimensions, takes into account the preferences of individual authors, as well as the quality of the entity under review, and encodes the flow of thoughts in a review by constraining the aspect distribution dynamically among successive review segments. We derive a similarity measure that considers both lexical and semantic similarity to find supporting opinions. Experiments on TripAdvisor hotel reviews and Yelp restaurant reviews show that our model outperforms existing methods for modeling opinions, and the proposed framework is effective in finding supporting opinions.
Anthology ID:
D17-1049
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:
472–481
Language:
URL:
https://www.aclweb.org/anthology/D17-1049
DOI:
10.18653/v1/D17-1049
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1049.pdf