A Joint Model for Aspect-Category Sentiment Analysis with Shared Sentiment Prediction Layer

Yuncong Li, Zhe Yang, Cunxiang Yin, Xu Pan, Lunan Cui, Qiang Huang, Ting Wei


Abstract
Aspect-category sentiment analysis (ACSA) aims to predict the aspect categories mentioned in texts and their corresponding sentiment polarities. Some joint models have been proposed to address this task. Given a text, these joint models detect all the aspect categories mentioned in the text and predict the sentiment polarities toward them at once. Although these joint models obtain promising performances, they train separate parameters for each aspect category and therefore suffer from data deficiency of some aspect categories. To solve this problem, we propose a novel joint model which contains a shared sentiment prediction layer. The shared sentiment prediction layer transfers sentiment knowledge between aspect categories and alleviates the problem caused by data deficiency. Experiments conducted on SemEval-2016 Datasets demonstrate the effectiveness of our model.
Anthology ID:
2020.ccl-1.103
Volume:
Proceedings of the 19th Chinese National Conference on Computational Linguistics
Month:
October
Year:
2020
Address:
Haikou, China
Venue:
CCL
SIG:
Publisher:
Chinese Information Processing Society of China
Note:
Pages:
1112–1121
Language:
English
URL:
https://www.aclweb.org/anthology/2020.ccl-1.103
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.ccl-1.103.pdf