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Sentiment Analysis on Multi-View Social Data

  • Teng Niu
  • Shiai Zhu
  • Lei Pang
  • Abdulmotaleb El Saddik
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9517)

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.

Keywords

Sentiment analysis Multi-View data Social media 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Teng Niu
    • 1
  • Shiai Zhu
    • 1
  • Lei Pang
    • 2
  • Abdulmotaleb El Saddik
    • 1
  1. 1.MCRLabUniversity of OttawaOttawaCanada
  2. 2.Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong

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