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International Conference on Web Information Systems Engineering

WISE 2016: Web Information Systems Engineering – WISE 2016 pp 3–18Cite as

Dynamic Topic-Based Sentiment Analysis of Large-Scale Online News

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10042)

Abstract

Many of today’s online news websites and aggregator apps have enabled users to publish their opinions without respect to time and place. Existing works on topic-based sentiment analysis of product reviews cannot be applied to online news directly because of the following two reasons: (1) The dynamic nature of news streams require the topic and sentiment analysis model also to be dynamically updated. (2) The user interactions among news comments can easily lead to inaccurate topic and sentiment extraction. In this paper, we propose a novel probabilistic generative model (DTSA) to extract topics and the specified sentiments from news streams and analyze their evolution over time simultaneously. DTSA incorporates a multiple timescale model into a generative topic model. Additionally, we further consider the links among news comments to avoid the error caused by user interactions. Finally, we derive distributed online inference procedures to update the model with newly arrived data and show the effectiveness of our proposed model on real-world data sets.

Keywords

  • Topic-based sentiment analysis
  • Topic model
  • User interaction
  • Online inference

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Notes

  1. 1.

    http://open-platform.theguardian.com/.

  2. 2.

    http://apiwiki.twitter.com/.

  3. 3.

    http://nlp.stanford.edu/software/tagger.shtml.

  4. 4.

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

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Author information

Authors and Affiliations

  1. Department of Computer and Information Science, NTNU, Trondheim, Norway

    Peng Liu, Jon Atle Gulla & Lemei Zhang

Authors
  1. Peng Liu
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  2. Jon Atle Gulla
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  3. Lemei Zhang
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Corresponding author

Correspondence to Peng Liu .

Editor information

Editors and Affiliations

  1. Poznań University of Economics, Poznan, Poland

    Wojciech Cellary

  2. University of Minnesota, Minneapolis, Minnesota, USA

    Mohamed F. Mokbel

  3. Tsinghua University, Beijing, China

    Jianmin Wang

  4. Victoria University, Melbourne, Victoria, Australia

    Hua Wang

  5. Victoria University, Melbourne, Victoria, Australia

    Rui Zhou

  6. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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Liu, P., Gulla, J.A., Zhang, L. (2016). Dynamic Topic-Based Sentiment Analysis of Large-Scale Online News. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10042. Springer, Cham. https://doi.org/10.1007/978-3-319-48743-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-48743-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48742-7

  • Online ISBN: 978-3-319-48743-4

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