Open Access
ITM Web Conf.
Volume 44, 2022
International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
Article Number 03061
Number of page(s) 5
Section Computing
Published online 05 May 2022
  1. Vikrant Chaugule, Abhishek D., Aadheeshwar Vijayakumar, Pravin Ramteke, and Shashidhar Koolagudi, Optimized Facial Expression Detection, (Aug 13, 2016) [Google Scholar]
  2. Preeti Thakre and Pankaj Agarkar, Customer Emotions Recognition Using Facial and Textual Review (Feb 14, 2020) [Google Scholar]
  3. P. Viola and M. J. Jones, “Robust real-time face detection,” International journal of computer vision, vol. 57, no. 2, pp. 137–154, 2004. [CrossRef] [Google Scholar]
  4. Zhaolong Li, “Analyzing Emotion on Amazon Product Review for User Modeling”, Master's Thesis, October 2013. [Google Scholar]
  5. Y. Zhang and B. C. Wallace, “A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification”, ArXiv preprint arXiv:1510.03820v4, 2016. [Google Scholar]
  6. L. C. De Silva, T. Miyasato, and R. Nakatsu, “Facial emotion recognition using multi-modal information,” in Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on, vol. 1. IEEE, 1997, pp. 397–401. [Google Scholar]
  7. K. Durand, M. Gallay, A. Seigneuric, F. Robichon, and J.-Y. Baudouin, “The development of facial emotion recognition: The role of configural information,” Journal of experimental child psychology, vol. 97, no. 1, pp. 14–27, 2007. [CrossRef] [Google Scholar]
  8. D. Bamman and N. A. Smith, “Contextualized Sarcasm Detection on Amazon Product Review”, in Proc. of the 9th Int. AAAI Conf. on Web and Social Media. Citeseer, 2015 [Google Scholar]
  9. Niko Colneric and Janez Demsar, “Emotion Recognition on Amazon Product Review: Comparative Study and Training a Unison Model”, IEEE transactions on affective computing, February 2018. [Google Scholar]
  10. A. Radford, R. Jozefowicz, and I. Sutskever, “Learning to Generate Reviews and Discovering Sentiment”, 2017. [Google Scholar]
  11. B. Nejat, G. Carenini, and R. Ng, “Exploring Joint Neural Model for Sentence Level Discourse Parsing and Sentiment Analysis”, Proc .of the SIGDIAL 2017. [Google Scholar]
  12. N. Nodarakis, S. Sioutas, A. Tsakalidis, and G. Tzimas, “Using Hadoop for Large Scale Analysis on Amazon Product Review: A Technical Report”, arXiv preprint arXiv:1602.01248, 2016. [Google Scholar]
  13. X. Liu, J. Gao, X. He, L. Deng, K. Duh, and Y. Y. Wang, “Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval”, Proc.of the Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 921, 2015. [Google Scholar]
  14. S. M. Mohammad and S. Kiritchenko, “Using Hashtags to Capture Fine Emotion Categories from Tweets”, Computational Intelligence, vol. 31, no. 2, pp. 301326, 2015. [CrossRef] [MathSciNet] [Google Scholar]
  15. M. Dixit, P. Rai, and S. Silakari, “A review on smiley face recognition,” in Communication Systems and Network Technologies (CSNT), 2013 International Conference on. IEEE, 2013, pp. 137–139. [CrossRef] [Google Scholar]

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