Deep Learning Techniques: A Review
Keywords:
Deep Learning, sentiment analysis, recurrent neural network, deep neural network, convolutional neural network, recursive neural network, deep belief networkAbstract
Deep Learning models are effective due to their automatic learning capability. This review paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis.
References
P. Vateekul and T. Koomsubha, A study of Sentiment Analysis Using Deep Learning techniques on Thai Twitter Data, 2016
X. Ouyang, P. Zhou, C. H. Li, and L, Liu, Sentiment Analysis Using Convolutional Neural Networks, Compite. Inf Technol. Ubiquitous Compute. Commun. 2015
T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient Estimation of Word Representations in Vector Space, Arxiv, no. 9, pp. 112, 2013.
J. Islam and Y. Zhang, Visual Sentiment Analysis for Social Images Using Transfer Learning Approach, 2016 IEEE Int. Conf. Big Data Cloud Comput. (BDCloud), Soc. Comput. Netw. (SocialCom), Sustain. Comput. Commun., pp. 124130, 2016.
A. Severyn and A. Moschitti, Twitter Sentiment Analysis with Deep Convolutional Neural Networks, Proc. 38th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. - SIGIR 15, pp. 959962, 2015.
L. Yanmei and C. Yuda, Research on Chinese Micro-Blog Sentiment Analysis Based on Deep Learning, 2015 8th Int. Symp. Comput. Intell. Des., pp. 358361, 2015.
Q. You, J. Luo, H. Jin, and J. Yang, Joint Visual-Textual Sentiment Analysis with Deep Neural Networks, Acm Mm, pp. 10711074, 2015.
C. Li, B. Xu, G. Wu, S. He, G. Tian, and H. Hao, Recursive deep learning for sentiment analysis over social data, Proc. - 2014 IEEE/WIC/ACM Int. Jt. Conf. Web Intell.
Intell. Agent Technol. - Work. WI IAT 2014, vol. 2, pp. 13881429, 2014.
R. Socher, A. Perelygin, and J. Wu, Recursive deep models for semantic compositionality over a sentiment treebank, Proc. , pp. 16311642, 2013.
W. Li and H. Chen, Identifying top sellers in underground economy using deep learning-based sentiment analysis, Proc. - 2014 IEEE Jt. Intell. Secur. Informatics Conf. JISIC 2014, pp. 6467, 2014.
C. Baecchi, T. Uricchio, M. Bertini, and A. Del Bimbo, A multimodal feature learning approach for sentiment analysis of social network multimedia, Multimed. Tools Appl., vol. 75, no. 5, pp. 25072525, 2016.
H. Yanagimoto, M. Shimada, and A. Yoshimura, Document similarity estimation for sentiment analysis using neural network, 2013 IEEE/ACIS 12th Int. Conf. Comput. Inf. Sci., pp. 105110, 2013.
R. Silhavy, R. Senkerik, Z. K. Oplatkova, P. Silhavy, and Z. Prokopova, Artificial intelligence perspectives in intelligent systems: Proceedings of the 5th computer science on-line conference 2016 (CSOC2016), vol 1, Adv. Intell. Syst. Comput., vol. 464, pp. 249261, 2016.
A. Hassan, M. R. Amin, A. Kalam, A. Azad, and N. Mohammed, Bangla Text ( BRBT ) using Deep Recurrent models.
T. Chen, R. Xu, Y. He, Y. Xia, and X. Wang, Using a Sequence Model for Sentiment Analysis, no. August, pp. 3444, 2016.
P. Ruangkanokmas, T. Achalakul, and K. Akkarajitsakul, Deep Belief Networks with Feature Selection for Sentiment Classification, Uksim.Info, pp. 16, 2016.
G. Zhou, Z. Zeng, J. X. Huang, and T. He, Transfer Learning for Cross-Lingual Sentiment Classification with Weakly Shared Deep Neural Networks, Proc. 39th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. – SIGIR 16, pp. 245254, 2016.
R. Ghosh, K. Ravi, and V. Ravi, A novel deep learning architecture for sentiment classification, 3rd IEEE Int. Conf. Recent Adv. Inf. Technol., pp. 511516, 2016.
R. Goebel and W. Wahlster, Integrated Uncertainty in Knowledge Modelling and Decision Making, Proc. Int. Symp. Integr. Uncertain. Knowl. Model. Decis. Mak. (IUKM 2011), vol. 1, pp. 362373, 2011.
A. Graves, N. Jaitly, and A. R. Mohamed, Hybrid speech recognition with Deep Bidirectional LSTM, 2013 IEEE Work. Autom. Speech Recognit. Understanding, ASRU 2013 - Proc., pp. 273278, 2013.
C. N. dos Santos and M. Gatti, Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts, Coling-2014, pp. 6978, 2014.
K. Ravi and V. Ravi, Sentiment classification of Hinglish text, 2016 3rd Int. Conf. Recent Adv. Inf. Technol. RAIT 2016, pp. 641645, 2016
S. Aftergood, ``Cybersecurity: The cold war online,`` Nature, vol. 547,no. 7661, pp. 30_31, Jul. 2017.
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