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Construction of unsupervised sentiment classifier on idioms resources

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Abstract

Sentiment analysis is the computational study of how opinions, attitudes, emotions, and perspectives are expressed in language, and has been the important task of natural language processing. Sentiment analysis is highly valuable for both research and practical applications. The focuses were put on the difficulties in the construction of sentiment classifiers which normally need tremendous labeled domain training data, and a novel unsupervised framework was proposed to make use of the Chinese idiom resources to develop a general sentiment classifier. Furthermore, the domain adaption of general sentiment classifier was improved by taking the general classifier as the base of a self-training procedure to get a domain self-training sentiment classifier. To validate the effect of the unsupervised framework, several experiments were carried out on publicly available Chinese online reviews dataset. The experiments show that the proposed framework is effective and achieves encouraging results. Specifically, the general classifier outperforms two baselines (a Naïve 50% baseline and a cross-domain classifier), and the bootstrapping self-training classifier approximates the upper bound domain-specific classifier with the lowest accuracy of 81.5%, but the performance is more stable and the framework needs no labeled training dataset.

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Authors and Affiliations

  1. School of Computer Science, National University of Defense Technology, Changsha, 410073, China

    Song-xian Xie  (谢松县) & Ting Wang  (王挺)

Authors
  1. Song-xian Xie  (谢松县)
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  2. Ting Wang  (王挺)
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Corresponding author

Correspondence to Song-xian Xie  (谢松县).

Additional information

Foundation item: Projects(61170156, 60933005) supported by the National Natural Science Foundation of China

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Xie, Sx., Wang, T. Construction of unsupervised sentiment classifier on idioms resources. J. Cent. South Univ. 21, 1376–1384 (2014). https://doi.org/10.1007/s11771-014-2075-4

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  • DOI: https://doi.org/10.1007/s11771-014-2075-4

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