Abstract
With the rapid development of micro-blog, blog and other types of social media, users’ reviews on the social media increase dramatically. Users’ reviews mining plays an important role in the application of product information or public opinion monitoring. Sentiment classification of users’ reviews is one of key issues in the review mining. Comparative study on sentiment classification results of reviews in different domains and the adaptability of sentiment classification methods is an interesting research topic. This paper classifies users’ reviews in three different domains based on Support Vector Machine with six kinds of feature weighting methods. Experiment results in three domains indicate that different domains have their own characteristics and the selection of feature weighting methods should consider the domain characteristics.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Wang, S.: Text sentiment classification research on web-based reviews. Shanghai University, Shanghai, pp. 1–5 (2008) (in Chinese)
Wang, H., Liu, X., Yin, P., Liao, Y.: Web text sentiment classification research. Scientific and Technical Information 29(5), 931–938 (2010) (in Chinese)
Zhang, Y.: Text sentiment classification research. Beijing Jiaotong University, Beijing, pp. 1–10 (2010) (in Chinese)
Agrawal, R., Rajagopalan, S., Srikant, R,. Xu, Y.: Mining newsgroups using networks arising from social behavior. In: Proceeding of the 12th WWW Conference, Budapest, Hungary, pp. 529–535 (2003)
Wiebe, J.M., Bruce, R.F., O’Hara, T.P.: Development and use of a gold-standard data set for subjectivity classifications. In: Proceedings of the Association for Computational Linguistics, pp. 246–253. College Park, Maryland (1999)
Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, pp. 105–112. ACL, USA (2003)
Wiebe, J., Wilson, T., Bruce, R.F., Bell, M., Martin, M.: Learning subjective language. Computational Linguistics 30(3), 277–308 (2004)
Turney, P.D.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of Annual Meeting of the Association for Computational Linguistics, Philadelphia, Pennsylvania, pp. 417–424 (2002)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, pp. 79–86. ACL, USA (2002)
Lin, W.H., Wilson, T.,Wiebe, J.,et al.: Which side are you on? Identifying perspectives at the document and sentence levels. In: Proceedings of the 10th Conference on Computational Natural Language Learning, NY, USA, pp. 109–116 (2006)
Pang, B., Lee, L.: Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In: Proceedings of Conference on Association for Computational Linguistics,. Michigan, pp. 115–124 (2005)
Ni, X., Xue, G.R., Ling, X., et al.: Exploring in the weblog space by detecting informative and affective articles. In: Proceedings of the 16th International Conference on World Wide Web, pp. 281–290. ACM (2007)
Tsai, C.H.: MMSEG: a word identification system for mandarin Chinese text based on two variants of the maximum matching algorithm (2000). http://www.geocities.com/hao150/mmseg/
Zhang, Z.: Tmsvm Reference Documents. tmsvm.googlecode.com/svn/trunk/Tmsvm Reference Documents (v1.1.0).docx Accessed: (May 1, 2013) (in Chinese)
Yang, Y., Pederson, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the Fourteenth International Conference, pp. 412–420 (1997)
Ng, H.T., Goh, W.B., Leong, K.: Feature selection, perceptron learning, and a usability case study for text categorization. ACM SIGIR Forum 31(SI), 67–73 (1997)
Chen, T., Xie, Y.: Feature dimension reduction methods for text classification. Scientific and Technical Information 24(6), 690–695 (2005)
Deng, Z.-H., Tang, S.-W., Yang, D.-Q., Zhang, M., Li, L.-Y., Xie, K.-Q.: A comparative study on feature weight in text categorization. In: Proceedings of the 6th Asia-Pacific Web Conference, Hangzhou, China, pp. 588–597 (2004)
Lan, M., Tan, C.-L., Low, H.-B.: Proposing a New TermWeighting Scheme for Text Categorization. In: Proceedings of AAAI Conference on Artificial Intelligence, Boston, Massachusetts, pp. 763–768 (2006)
Liu, B., Hao, Z., Xiao, Y.: Interactive iteration on one against one classification algorithm. Pattern Recognition and Artificial Intelligence, 21(4):425–431 (2008) (in Chinese)
Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw Hill Book Co. (1983)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, Q., Zhang, C. (2014). Sentiment Classification of Chinese Reviews in Different Domain: A Comparative Study. In: Wang, X., Pedrycz, W., Chan, P., He, Q. (eds) Machine Learning and Cybernetics. ICMLC 2014. Communications in Computer and Information Science, vol 481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45652-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-662-45652-1_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45651-4
Online ISBN: 978-3-662-45652-1
eBook Packages: Computer ScienceComputer Science (R0)