Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews

Runcong Zhao, Lin Gui, Gabriele Pergola, Yulan He


Abstract
In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brand-associated polarity-bearing topics from product reviews. Different from existing models for sentiment-topic extraction which assume topics are grouped under discrete sentiment categories such as ‘positive’, ‘negative’ and ‘neural’, BTM is able to automatically infer real-valued brand-associated sentiment scores and generate fine-grained sentiment-topics in which we can observe continuous changes of words under a certain topic (e.g., ‘shaver’ or ‘cream’) while its associated sentiment gradually varies from negative to positive. BTM is built on the Poisson factorisation model with the incorporation of adversarial learning. It has been evaluated on a dataset constructed from Amazon reviews. Experimental results show that BTM outperforms a number of competitive baselines in brand ranking, achieving a better balance of topic coherence and unique-ness, and extracting better-separated polarity-bearing topics.
Anthology ID:
2021.eacl-main.199
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2341–2351
Language:
URL:
https://aclanthology.org/2021.eacl-main.199
DOI:
10.18653/v1/2021.eacl-main.199
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.199.pdf