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Fusing Phonetic Features and Chinese Character Representation for Sentiment Analysis

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Computational Linguistics and Intelligent Text Processing (CICLing 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13452))

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Abstract

The Chinese pronunciation system offers two characteristics that distinguish it from other languages: deep phonemic orthography and intonation variations. We are the first to argue that these two important properties can play a major role in Chinese sentiment analysis. Hence, we learn phonetic features of Chinese characters and fuse them with their textual and visual features in order to mimic the way humans read and understand Chinese text. 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.

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Notes

  1. 1.

    Neutral tone, in addition to the four variations, is neglected for the moment, due to its lack of connection with sentiment.

  2. 2.

    https://github.com/fxsjy/jieba.

  3. 3.

    https://en.wikipedia.org/wiki/Phonemic_orthography.

  4. 4.

    https://chinese.yabla.com/.

  5. 5.

    https://github.com/mozillazg/python-pinyin.

  6. 6.

    Both the datasets and codes in this paper are available for public download upon acceptance.

  7. 7.

    https://github.com/mozillazg/python-pinyin.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Haiyun Peng, Soujanya Poria, Yang Li & Erik Cambria

Authors
  1. Haiyun Peng
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  2. Soujanya Poria
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  3. Yang Li
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  4. Erik Cambria
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Corresponding author

Correspondence to Erik Cambria .

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Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Peng, H., Poria, S., Li, Y., Cambria, E. (2023). Fusing Phonetic Features and Chinese Character Representation for Sentiment Analysis. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13452. Springer, Cham. https://doi.org/10.1007/978-3-031-24340-0_12

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  • DOI: https://doi.org/10.1007/978-3-031-24340-0_12

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