Deep Learning Networks For Visual Sentiment Analysis: CaffeNet and TensorFlow
Keywords:
NLP, VSA, Neural Network, Bottlenecks, TensorFlow, CaffeNetAbstract
Predicting emotion, opinion, sentiment from the comments, tweets, blogs, or bunch of words written by user has importance in Machine Learning. In Natural Language Processing emotion of particular user or person is extracted from the text, sentence, and document which all being written by user. NLP uses written text word, n-grams, combination of words as features and tries to find out relation that may exist among them. But as technology is evolving day by day it is very easy to capture or click picture, selfie through cellphone, smartphone, tablets, phablets, camera and all digital devices. These pictures of self or group are uploading on social media at every second with a suitable caption. So it’s becoming very easy to express through pictures, videos, images than to write thousand words. So to analyses sentiment of pictures on social media the NLP is way lack behind. It needs to be deal in objects rather than plain text. So it’s been need to process image and extracts sentiment from these pictures. So his paper will deal with the image and recognizing face as object and try to find emotion or sentiment of that picture.
References
- P. Turney, “Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews”, In Isabelle P, ed. Proc. Of the ACL. Morristown: ACL, 2002, 417-424.
- S. M. Kim, E. Hovy, “Automatic detection of opinion bearing wordsand sentences”, In Carbonell JG, Siekmann, IJCNLP, 2005, ACL, 61-66.
- H. Yu, and V. Hatzivassiloglou, “Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences,” In Collins M, Steedman M, eds. Proc. of the EMNLP, 2003. Morristown: ACL, 129—136.
- M. Hu, B. Liu, “Mining and summarizing customer reviews”, In Kohavi R, ed. Proc. of the KDD, 2004. New York: ACM Press, 168—177.
- Jana Machajdik and Allan Hanbury, “Affective image classification using features inspired by psychology and art theory,” In Proceedings of the international conference on Multimedia. ACM, 2010, pp. 83–92.
- J. Jia, S. Wu, X. Wang, P. Hu, L. Cai, and J. Tang, “Can we understand van Gogh's Mood?: Learning to infer A_ects from Images in Social Networks”, In ACM MM, 2012, 857-860.
- D. Borth, R. Ji., “Large-Scale Visual Sentiment Ontology And Detectors Using Adjective Noun Pairs” , In Proc. of the 21st ACM International Conference on Multimedia, 2013, 223-232.
- D. Cao, R. Ji, D. Lin, S. Li, “A cross-media public sentiment analysis system for microblog”, In ACM Multimedia Systems Journal, Special Issue Paper, DOI: 10.1007/s00530-014- 0407-8 , 2014.
- Rongrong Ji, Donglin Cao, Dazhen Lin, “Cross-modality sentiment analysis for social multimedia”, In 2015 IEEE.
- Quanzeng You and Jiebo Luo, Hailin Jin, Jianchao Yang, “Cross-modality Consistent Regression for Joint Visual-Textual Sentiment Analysis of Social Multimedia”, In 2016 ACM.
- Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev et al., “Caffe: Convolutional Architecture for Fast Feature Embedding”, In arXiv:1408.5093, 2014.
- Tao Chen, Damian Borth, Trevor Darrell, and Shih-Fu Chang, “Deepsentibank: Visual sentiment concept classifica-tion with deep convolutional neural networks”, In arXiv preprint arXiv:1410.8586, 2014.
- Christian Szegedy, Wei Liu, Yangqing Jia, Andrew Rabinovich et al., “Going Deeper with Convolution”, In 2015, IEEE Explore.
- Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen et al., “TensorFlow: A System for Large-Scale Machine Learning”, In 2016, 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI ’16).
- Weblink - https://github.com/sjchoi86
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