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Visual sentiment topic model based microblog image sentiment analysis

Research paper by Donglin Cao, Rongrong Ji, Dazhen Lin, Shaozi Li

Indexed on: 04 Nov '14Published on: 04 Nov '14Published in: Multimedia Tools and Applications



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.