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Sentiment Analysis on Multi-View Social Data

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


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.


  • Sentiment analysis
  • Multi-View data
  • Social media

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  • DOI: 10.1007/978-3-319-27674-8_2
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Correspondence to Shiai Zhu .

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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.

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  • Print ISBN: 978-3-319-27673-1

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