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).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, vol. 2, pp. 90–94. Association for Computational Linguistics (2012)
Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on Association for Computational Linguistics, p. 271. Association for Computational Linguistics (2004)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168–177. ACM (2004)
Agarwal, A., Biadsy, F., Mckeown, K.R.: Contextual phrase-level polarity analysis using lexical affect scoring and syntactic n-grams. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pp. 24–32. Association for Computational Linguistics (2009)
McCann, S., Lowe, D.G.: Local naive bayes nearest neighbor for image classification. In: CVPR2012, pp. 3650–3656. IEEE (2012)
Eshghi, K.: Support vector machine. Google Patents. US Patent App. 13/873,587, April 2013
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
Martineau, J., Finin, T.: Delta tfidf: an improved feature space for sentiment analysis. In: ICWSM (2009)
Wang, G., Xie, S., Liu, B., Yu, P.S.: Identify online store review spammers via social review graph. ACM Trans. Intell. Syst. Technol. (TIST) 3(4), 61 (2012)
Nakagawa, T., Inui, K., Kurohashi, S.: Dependency tree-based sentiment classification using crfs with hidden variables. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 786–794. Association for Computational Linguistics (2010)
Huang, X., Shi, L., Suykens, J.A.K.: Sequential minimal optimization for svm with pinball loss. Neurocomputing 149, 1596–1603 (2015)
Accentax, Adai0808, adewinter, et al.: Portia: Visual scraping for scrapy (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-662-48683-2_28
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-48682-5
Online ISBN: 978-3-662-48683-2
eBook Packages: Computer ScienceComputer Science (R0)