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Visual sentiment topic model based microblog image sentiment analysis

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Abstract

With a growing number of images being used to express opinions in Microblog, text based sentiment analysis is not enough to understand the sentiments of users. To obtain the sentiments implied in Microblog images, we propose a Visual Sentiment Topic Model (VSTM) which gathers images in the same Microblog topic to enhance the visual sentiment analysis results. First, we obtain the visual sentiment features by using Visual Sentiment Ontology (VSO); then, we build a Visual Sentiment Topic Model by using all images in the same topic; finally, we choose better visual sentiment features according to the visual sentiment features distribution in a topic. The best advantage of our approach is that the discriminative visual sentiment ontology features are selected according to the sentiment topic model. The experiment results show that the performance of our approach is better than VSO based model.

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Acknowledgments

This work was supported by National Nature Science Foundation of China (No.61402386, No. 61305061 and No. 61202143), the Nature Science Foundation of Fujian Province (No. 2014 J01249 and No. 2011 J01367), Doctoral Program Foundation of Institutions of Higher Education of China (No.20090121110032), Shenzhen Science and Technology Research Foundation (No.JC200903180630A) and Special Fund for Developing Shenzhen’s Strategic Emerging Industries (No. JCYJ20120614164600201).

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Authors and Affiliations

  1. Cognitive Science Department, Xiamen University, Xiamen, 361005, China

    Donglin Cao, Rongrong Ji, Dazhen Lin & Shaozi Li

  2. Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen, 361005, China

    Donglin Cao, Rongrong Ji, Dazhen Lin & Shaozi Li

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  1. Donglin Cao
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  2. Rongrong Ji
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  3. Dazhen Lin
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  4. Shaozi Li
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Correspondence to Rongrong Ji.

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Cao, D., Ji, R., Lin, D. et al. Visual sentiment topic model based microblog image sentiment analysis. Multimed Tools Appl 75, 8955–8968 (2016). https://doi.org/10.1007/s11042-014-2337-z

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  • DOI: https://doi.org/10.1007/s11042-014-2337-z

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