Phonetic-enriched text representation for Chinese sentiment analysis with reinforcement learning

Haiyun Peng, Yukun Ma, Soujanya Poria, Yang Li, Erik Cambria

科研成果: 期刊稿件文章同行评审

47 引用 (Scopus)

摘要

The Chinese pronunciation system offers two characteristics that distinguish it from other languages: deep phonemic orthography and intonation variations. In this paper, we hypothesize that these two important properties can play a major role in Chinese sentiment analysis. In particular, we propose two effective features to encode phonetic information and, hence, fuse it with textual information. With this hypothesis, we propose Disambiguate Intonation for Sentiment Analysis (DISA), a network that we develop based on the principles of reinforcement learning. DISA disambiguates intonations for each Chinese character (pinyin) and, hence, learns precise phonetic representations. We also fuse phonetic features with textual and visual features to further improve performance. Experimental results on five different Chinese sentiment analysis datasets show that the inclusion of phonetic features significantly and consistently improves the performance of textual and visual representations and surpasses the state-of-the-art Chinese character-level representations.

源语言英语
页(从-至)88-99
页数12
期刊Information Fusion
70
DOI
出版状态已出版 - 6月 2021

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