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Aspect and Sentiment Unification Model for Twitter Analysis

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Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques (IScIDE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9243))

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Abstract

With the special “@, #, //” symbols, which include a lot of emotional symbols and pictures etc., tweets are different with other user-generated general texts, such as blogs, forums, reviews. Considering structural features and content of tweets, we present a semi-supervised Aspect and Sentiment Unification Model(PL-SASU). Using more information rather than solo texts, this model can model tweets better. The experiments of sentiment classification and aspect identification on real twitter data show that PL-SASU outperforms JTS, ASUM and UTSU model.

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Notes

  1. 1.

    http://tcci.ccf.org.cn/conference/2013/dldoc/evdata02.zip.

  2. 2.

    Sina Weibo reserve emotional symbol picture in the form of “[characters]”, for example is reserved as “[hehe]”.

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Acknowledgments

This work was supported by National Key Basic Research Program (2015CB358700) and Natural Science Foundation (61472206, 61073071) of China.

Author information

Authors and Affiliations

  1. State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory of Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Hui Zhang, Yi-qun Liu & Shao-ping Ma

  2. Operation Experiment Center, Nanjing Army Command College, Nanjing, China

    Hui Zhang

  3. Department of Physics, Fudan University, Shanghai, China

    Tong-xin Wang

Authors
  1. Hui Zhang
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  2. Tong-xin Wang
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  3. Yi-qun Liu
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  4. Shao-ping Ma
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Corresponding author

Correspondence to Hui Zhang .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

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Zhang, H., Wang, Tx., Liu, Yq., Ma, Sp. (2015). Aspect and Sentiment Unification Model for Twitter Analysis. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_4

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  • DOI: https://doi.org/10.1007/978-3-319-23862-3_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23861-6

  • Online ISBN: 978-3-319-23862-3

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