ABSTRACT
Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using additional images and videos to express their opinions and share their experiences. Sentiment analysis of such large-scale textual and visual content can help better extract user sentiments toward events or topics. Motivated by the needs to leverage large-scale social multimedia content for sentiment analysis, we utilize both the state-of-the-art visual and textual sentiment analysis techniques for joint visual-textual sentiment analysis. We first fine-tune a convolutional neural network (CNN) for image sentiment analysis and train a paragraph vector model for textual sentiment analysis. We have conducted extensive experiments on both machine weakly labeled and manually labeled image tweets. The results show that joint visual-textual features can achieve the state-of-the-art performance than textual and visual sentiment analysis algorithms alone.
- D. Borth, T. Chen, R. Ji, and S.-F. Chang. Sentibank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content. In ACM MM, pages 459--460, 2013. Google ScholarDigital Library
- D. Borth, R. Ji, T. Chen, T. Breuel, and S.-F. Chang. Large-scale visual sentiment ontology and detectors using adjective noun pairs. In ACM MM, pages 223--232. ACM, 2013. Google ScholarDigital Library
- D. Cao, R. Ji, D. Lin, and S. Li. A cross-media public sentiment analysis system for microblog. Multimedia Systems, pages 1--8, 2014.Google Scholar
- D. C. Cireşan, U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber. Flexible, high performance convolutional neural networks for image classification. In IJCAI, pages 1237--1242, 2011. Google ScholarDigital Library
- X. Hu, J. Tang, H. Gao, and H. Liu. Unsupervised sentiment analysis with emotional signals. In WWW, pages 607--618, 2013. Google ScholarDigital Library
- C. Hutto and E. Gilbert. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In ICWSM, 2014.Google Scholar
- Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093, 2014.Google Scholar
- D. Joshi, R. Datta, E. Fedorovskaya, Q.-T. Luong, J. Z. Wang, J. Li, and J. Luo. Aesthetics and emotions in images. IEEE Signal Processing Magazine, 28(5):94--115, 2011.Google ScholarCross Ref
- A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1097--1105, 2012.Google ScholarDigital Library
- Q. Le and T. Mikolov. Distributed representations of sentences and documents. In ICML, 2014.Google ScholarDigital Library
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278--2324, 1998.Google ScholarCross Ref
- T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, pages 3111--3119, 2013.Google ScholarDigital Library
- L.-P. Morency, R. Mihalcea, and P. Doshi. Towards multimodal sentiment analysis: Harvesting opinions from the web. In ICMI, pages 169--176, 2011. Google ScholarDigital Library
- B. O'Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith. From tweets to polls: Linking text sentiment to public opinion time series. ICWSM, 11:122--129, 2010.Google ScholarCross Ref
- S. Siersdorfer, E. Minack, F. Deng, and J. Hare. Analyzing and predicting sentiment of images on the social web. In ACM MM, pages 715--718. ACM, 2010. Google ScholarDigital Library
- A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe. Predicting elections with twitter: What 140 characters reveal about political sentiment. ICWSM, 10:178--185, 2010.Google ScholarCross Ref
- M. Wang, D. Cao, L. Li, S. Li, and R. Ji. Microblog sentiment analysis based on cross-media bag-of-words model. In ICIMCS, pages 76:76--76:80. ACM, 2014. Google ScholarDigital Library
- Q. You, J. Luo, H. Jin, and J. Yang. Robust image sentiment analysis using progressively trained and domain transferred deep networks. In AAAI, 2015.Google ScholarDigital Library
- J. Yuan, S. Mcdonough, Q. You, and J. Luo. Sentribute: image sentiment analysis from a mid-level perspective. In WISDOM, page 10, 2013. Google ScholarDigital Library
Index Terms
- Joint Visual-Textual Sentiment Analysis with Deep Neural Networks
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