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International Symposium on Natural Language Processing Based on Naturally Annotated Big Data

China National Conference on Computational Linguistics

NLP-NABD 2016, CCL 2016: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data pp 255–266Cite as

Topic-Sentiment Mining from Multiple Text Collections

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10035))

Abstract

Topic-sentiment mining is a challenging task for many applications. This paper presents a topic-sentiment joint model in order to mine topics and their sentimental polarities from multiple text collections. Text collections are represented with a mixture of components and modeled via the hierarchical Dirichlet process which can determine the number of components automatically. Each component consists of topic words and its sentiments. The model can mine topics with different proportions and different sentimental polarities as well as one positive and one negative topic for each collection. Experiments on two text collections from Chinese news media and microblog show that our model can find meaningful topics and their different sentimental polarities. Experiments on Multi-Domain Sentiment Dataset show that our model is better than the JST-alike models on parameter settings for topic-sentiment mining.

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Notes

  1. 1.

    These collections may vary a lot in the content, but refer to similar topics.

  2. 2.

    http://www.cs.jhu.edu/~mdredze/datasets/sentiment/.

  3. 3.

    http://ir.dlut.edu.cn/EmotionOntologyDownload.aspx (in Chinese).

  4. 4.

    http://keenage.com/html/c_bulletin_2007.htm (in Chinese).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Qifeng Zhu & Fang Li

Authors
  1. Qifeng Zhu
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  2. Fang Li
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Correspondence to Qifeng Zhu .

Editor information

Editors and Affiliations

  1. Tsinghua University , Beijing, China

    Maosong Sun

  2. Fudan University , Shanghai, China

    Xuanjing Huang

  3. Dalian University of Technology , Dalian, China

    Hongfei Lin

  4. Tsinghua University , Beijing, China

    Zhiyuan Liu

  5. Tsinghua University , Beijing, China

    Yang Liu

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Zhu, Q., Li, F. (2016). Topic-Sentiment Mining from Multiple Text Collections. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_22

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  • DOI: https://doi.org/10.1007/978-3-319-47674-2_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47673-5

  • Online ISBN: 978-3-319-47674-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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