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Microblog Sentiment Classification Based on Supervised and Unsupervised Combination

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Trustworthy Computing and Services (ISCTCS 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 426))

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Abstract

Millions of users are sharing their views and opinions in microblog everyday, which makes sentiment classification in microblog be an important and practical issue in social networks. In this paper, we combined the conversional supervised algorithm with unsupervised methods to conduct sentiment analysis. Specifically, we divided the content into two parts: those with emoticons and those without emoticons, and use multiple optimization for the two different parts. Practical evaluation shows that our methods could perform effectively and efficiently for this attracting problem.

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Author information

Authors and Affiliations

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

    Shaojie Pei, Lumin Zhang & Aiping Li

  2. Department of Optical Engineering, Zhejiang University, Hangzhou, China

    Yusha Liu

Authors
  1. Shaojie Pei
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  2. Lumin Zhang
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  3. Aiping Li
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  4. Yusha Liu
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Corresponding author

Correspondence to Shaojie Pei .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Yuyu Yuan

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xu Wu

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Yueming Lu

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© 2014 Springer-Verlag Berlin Heidelberg

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Pei, S., Zhang, L., Li, A., Liu, Y. (2014). Microblog Sentiment Classification Based on Supervised and Unsupervised Combination. In: Yuan, Y., Wu, X., Lu, Y. (eds) Trustworthy Computing and Services. ISCTCS 2013. Communications in Computer and Information Science, vol 426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43908-1_18

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  • DOI: https://doi.org/10.1007/978-3-662-43908-1_18

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43907-4

  • Online ISBN: 978-3-662-43908-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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