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Sentiment analysis for Chinese reviews of movies in multi-genre based on morpheme-based features and collocations

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  • Heng-Li Yang

    (National Cheng Chi University)

  • August F. Y. Chao

    (National Cheng Chi University)

Abstract

The application of sentiment analysis, also known as opinion mining, is more difficult in Chinese than in Indo-European languages, due to the compounding nature of Chinese words and phrases, and relatively lack of reliable resources in Chinese. This study used seed words, Chinese morphemes, which are mono-syllabic characters that function as individual words or be combined to create Chinese words and phrases, to classify movie reviews found on Yahoo! Taiwan. We utilized higher Pointwise Mutual Information (PMI) collocations, which consist of selected morpheme-level compounded features to build classifiers. The contributions of this study include the following: (Bird 2006) proposing a method of generating domain-dependent Chinese morphemes directly from large data set without any predefined sentimental resources; (Bradley and Lang 1999) building morpheme-based classifiers applicable in various movie genres, and shown to produce better results than other classifiers based on keywords (NTUSD and HowNet) or feature selection (TFIDF); (Church and Hanks in Computational linguistics, 16(1), 22-29 1990) identifying compounds that have different semantic polarities depending on contexts.

Suggested Citation

  • Heng-Li Yang & August F. Y. Chao, 2015. "Sentiment analysis for Chinese reviews of movies in multi-genre based on morpheme-based features and collocations," Information Systems Frontiers, Springer, vol. 17(6), pages 1335-1352, December.
    • Handle: RePEc:spr:infosf:v:17:y:2015:i:6:d:10.1007_s10796-014-9498-1
    DOI: 10.1007/s10796-014-9498-1
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    References listed on IDEAS

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    1. Zimin Wu & Gwyneth Tseng, 1993. "Chinese text segmentation for text retrieval: Achievements and problems," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 44(9), pages 532-542, October.
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    Cited by:

    1. Youngseok Choi & Habin Lee, 0. "Data properties and the performance of sentiment classification for electronic commerce applications," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    2. DunLu Peng & YinRui Wang & Cong Liu & Zhang Chen, 0. "TL-NER: A Transfer Learning Model for Chinese Named Entity Recognition," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    3. Youngseok Choi & Habin Lee, 2017. "Data properties and the performance of sentiment classification for electronic commerce applications," Information Systems Frontiers, Springer, vol. 19(5), pages 993-1012, October.
    4. DunLu Peng & YinRui Wang & Cong Liu & Zhang Chen, 2020. "TL-NER: A Transfer Learning Model for Chinese Named Entity Recognition," Information Systems Frontiers, Springer, vol. 22(6), pages 1291-1304, December.

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