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DFDS: A Domain-Independent Framework for Document-Level Sentiment Analysis Based on RST

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Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10366))

Abstract

Document-level sentiment analysis is among the most popular research fields of nature language processing in recent years, in which one of major challenges is that discourse structural information can be hardly captured by existing approaches. In this paper, a domain-independent framework for document-level sentiment classification with weighting rules based on Rhetorical Structure Theory is proposed. First, original textual documents are parsed into rhetorical structure trees through a preprocessing pipeline. Next, the sentiment score of elementary discourse units is computed via sentence-level sentiment classification method. Finally, according to the rhetorical relation between neighbor discourse units, we define weighting schema and composing rules based on which scores of elementary discourse units are summed recursively to the whole document. Experiment results show that our approach has better performance on datasets in different domains, compared with state-of-art document-level sentiment analysis systems based on RST, and the best result is 15% higher than baseline.

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Notes

  1. 1.

    Available at http://stanfordnlp.github.io/CoreNLP/.

  2. 2.

    http://nlp.stanford.edu/sentiment.

  3. 3.

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

  4. 4.

    Available at https://github.com/jiyfeng/DPLP.

  5. 5.

    Available at http://mpqa.cs.pitt.edu/lexicons/subj_lexicon/.

  6. 6.

    Available at http://sentiwordnet.isti.cnr.it/.

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Acknowledgement

This work is supported bythe National Natural Science Foundation of China (NSFC) (61373165, 61373035 and 61672377).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Zhenyu Zhao & Guozheng Rao

  2. School of Computer Software, Tianjin University, Tianjin, China

    Zhiyong Feng

  3. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China

    Zhenyu Zhao, Guozheng Rao & Zhiyong Feng

Authors
  1. Zhenyu Zhao
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  2. Guozheng Rao
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  3. Zhiyong Feng
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Corresponding author

Correspondence to Guozheng Rao .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  2. Computer Science, Aarhus University, Aarhus N, Denmark

    Christian S. Jensen

  3. Computer Science, University of Southern California, Los Angeles, California, USA

    Cyrus Shahabi

  4. Northeastern University, Shenyang, China

    Xiaochun Yang

  5. Kent State University, Kent, Ohio, USA

    Xiang Lian

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Zhao, Z., Rao, G., Feng, Z. (2017). DFDS: A Domain-Independent Framework for Document-Level Sentiment Analysis Based on RST. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_23

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  • DOI: https://doi.org/10.1007/978-3-319-63579-8_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63578-1

  • Online ISBN: 978-3-319-63579-8

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