Abstract
Mining opinions and sentiment from cross-cultural communication Web sites can deepen mutual understanding among people between countries and provides an important channel for researching China’s cross-cultural communication. The sentiment analysis in the context of cross-cultural communication faces the challenges of culture-dependent, fine-grained sentiment understanding, and topic-centralization. Traditional approaches use machine learning methods, such as Naive Bayes, maximum entropy and support vector machine. In this paper, we exploit the machine learning methods in the context of cross-cultural communication, take the advantages of Naive Bayes and support vector machine methods and propose a novel NB-SVM based sentiment analysis algorithm. Extensive experiments show that the proposed approach performs well and can achieve \(0.3\,\%\) error rate of sentiment classification with appropriate parameter settings.
This work is supported by the Fundamental Research Funds for the Central Universities (No. 023600-500110002), the major program of National Social Science Funds of China (No. 14@ZH036), the National Natural Science Foundation of China (No. 61502038).
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Xu, Y., Wang, Z., Chen, Y. (2015). A Novel NB-SVM-Based Sentiment Analysis Algorithm in Cross-Cultural Communication. In: Niu, W., et al. Applications and Techniques in Information Security. ATIS 2015. Communications in Computer and Information Science, vol 557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48683-2_28
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DOI: https://doi.org/10.1007/978-3-662-48683-2_28
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