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Sentiment Analysis on Multi-View Social Data

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MultiMedia Modeling (MMM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9517))

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Abstract

There is an increasing interest in understanding users’ attitude or sentiment towards a specific topic (e.g., a brand) from the large repository of opinion-rich data on the Web. While great efforts have been devoted on the single media, either text or image, little attempts are paid for the joint analysis of multi-view data which is becoming a prevalent form in the social media. For example, paired with a short textual message on Twitter, an image is attached. To prompt the research on this interesting and important problem, we introduce a multi-view sentiment analysis dataset (MVSA) including a set of image-text pairs with manual annotations collected from Twitter. The dataset can be utilized as a valuable benchmark for both single-view and multi-view sentiment analysis. With this dataset, many state-of-the-art approaches are evaluated. More importantly, the effectiveness of the correlation between different views is also studied using the widely used fusion strategies and an advanced multi-view feature extraction method. Results of these comprehensive experiments indicate that the performance can be boosted by jointly considering the textual and visual views.

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Notes

  1. 1.

    http://www.sananalytics.com/lab.

  2. 2.

    https://about.twitter.com/company.

  3. 3.

    http://twitter4j.org/en/.

  4. 4.

    http://www.sba.pdx.edu/faculty/mblake/448/FeelingsList.pdf.

  5. 5.

    http://www.mcrlab.net/research/mvsa-sentiment-analysis-on-multi-view-social-data/.

  6. 6.

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

  7. 7.

    http://sentistrength.wlv.ac.uk/.

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Author information

Authors and Affiliations

  1. MCRLab, University of Ottawa, Ottawa, Canada

    Teng Niu, Shiai Zhu & Abdulmotaleb El Saddik

  2. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Lei Pang

Authors
  1. Teng Niu
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  2. Shiai Zhu
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  3. Lei Pang
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  4. Abdulmotaleb El Saddik
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Corresponding author

Correspondence to Shiai Zhu .

Editor information

Editors and Affiliations

  1. University of Texas at San Antonio, San Antonio, USA

    Qi Tian

  2. Dept. of Information Engineering, University of Trento, Povo, Trento, Italy

    Nicu Sebe

  3. EECS, University of Central Florida, Orlando, Florida, USA

    Guo-Jun Qi

  4. EURECOM, Sophia-Antipolis, France

    Benoit Huet

  5. Hefei University of Technology, Hefei, Anhui, China

    Richang Hong

  6. School of Computing and Information, Hefei University of Technology, Hefei, Anhui, China

    Xueliang Liu

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© 2016 Springer International Publishing Switzerland

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Niu, T., Zhu, S., Pang, L., El Saddik, A. (2016). Sentiment Analysis on Multi-View Social Data. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-27674-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27673-1

  • Online ISBN: 978-3-319-27674-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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