Open Access
| Issue |
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 | |
| DOI | https://doi.org/10.1051/itmconf/20224403061 | |
| Published online | 05 May 2022 | |
- Vikrant Chaugule, Abhishek D., Aadheeshwar Vijayakumar, Pravin Ramteke, and Shashidhar Koolagudi, Optimized Facial Expression Detection, (Aug 13, 2016) [Google Scholar]
- Preeti Thakre and Pankaj Agarkar, Customer Emotions Recognition Using Facial and Textual Review (Feb 14, 2020) [Google Scholar]
- 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]
- Zhaolong Li, “Analyzing Emotion on Amazon Product Review for User Modeling”, Master's Thesis, October 2013. [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- 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]
- A. Radford, R. Jozefowicz, and I. Sutskever, “Learning to Generate Reviews and Discovering Sentiment”, 2017. [Google Scholar]
- 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]
- 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]
- 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]
- 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]
- 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]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.