Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2018

Abstract

Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem. Results on English and Chinese microblog datasets confirm that our method outperforms state-of-the-art baseline algorithms with large margins.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)

First Page

1980

Last Page

1989

Publisher

Association for Computational Linguistics

City or Country

Melbourne, Australia

Additional URL

https://aclweb.org/anthology/P18-1184

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