Asian Journal of Information Technology

Year: 2021
Volume: 20
Issue: 10
Page No. 199 - 209

An Improved CNN and BLSTM Based Method to Perceive Mood of Patients in Online Social Networks

Authors : R.Sathish Kumar

References

Araque, O., I. Corcuera-Platas, J.F. Sanchez-Rada and C.A. Iglesias, 2017. Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst. Appl., 77: 236-246.
CrossRef  |  Direct Link  |  

Arnold,L., S. Rebecchi, S. Chevallier and H. P. Moisy, 2011. An Introduction to Deep Learning. https://hal.archives-ouvertes.fr/hal-01352061/document

Baecchi, C., T. Uricchio, M. Bertini and A.D. Bimbo, 2015. A multimodal feature learning approach for sentiment analysis of social network multimedia. Multimed. Tools Appl, 75: 2507-2525.
CrossRef  |  Direct Link  |  

Bengio, S., Li Deng, H. Larochelle, H. Lee and R. Salakhutdinov, 2013. Guest editors' Introduction: special section on learning deep architectures. IEEE Trans. Pattern Anal. Mach. Intell, 35: 1795-1797.
CrossRef  |  Direct Link  |  

Berbano,A.E.U., H.N.V. Pengson, C.G.V. Razon, K.C.G. Tungcul and S.V. Prado, 2017. Classification of stress into emotional, mental, physical and no stress using electroencephalogram signal analysis. Classification of stress into emotional, mental, physical and no stress using electroencephalogram signal analysis. 2017 IEEE pp; 11-14.

Day,M.Y., C.C. Lee, 2016. Deep learning for financial sentiment analysis on finance news providers. https://ieeexplore.ieee.org/document/7752381

Deng, L., G. Hinton and B. Kingsbury, 2013. New types of deep neural network learning for speech recognition and related applications: An overview. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 26-31, 2013, IEEE, Vancouver, British Columbia, Canada, ISBN:978-1-4799-0356-6, pp: 8599-8603.

Ghosh,R.,K. Ravi and V.Ravi, 2016. A novel deep learning architecture for sentiment classification. A novel deep learning architecture for sentiment classification. 2016 IEEE pp; 511-516.

Glavan,R., A. Mirica, and B. Firtescu, 2016. The use of social media for communication In official statistics at european level. Review vol, 64(4), pages 37-48, December: pp;37-48.
Direct Link  |  

Graves, N. Jaitly and A.R. Mohamed, 2014. Hybrid speech recognition with Deep Bidirectional LSTM. Hybrid speech recognition with Deep Bidirectional LSTM. 2014 IEEE pp; 273-278.

Guimaraes,R.G., R.L. Rosa, D.De Gaetano, D.Z. Rodriguez and G. Bressan, 2017. Age Groups Classification in Social Network Using Deep Learning. In: Age Groups Classification in Social Network Using Deep Learning. Guimaraes,R.G., R.L. Rosa, D.De Gaetano, D.Z. Rodriguez and G. Bressan, IEEE Canada 12.

Guo,Y., Yu Liu, A. Oerlemans, S. Lao, S. Wu and M.S. Lew, 2015. Deep learning for visual understanding: A review. Neurocomputing, 187: 27-48.
CrossRef  |  Direct Link  |  

Haenlein,M and A.M. Kaplan, 2010. An empirical analysis of attitudinal and ehavioral reactions toward the abandonment of unprofitable customer relationships. J. of Relationship Marketing, 9: 200-228.
CrossRef  |  Direct Link  |  

Heredia,B., T.M. Khoshgoftaar, J. Prusa and M. Crawford, 2016. Cross-domain sentiment analysis: An empirical investigation. Int. Conf. Inf. Reuse. Integr, 1: pp; 160-165.
CrossRef  |  Direct Link  |  

Huang,Y.P., T. Goh and C.Li Liew, 2008. Hunting Suicide Notes in Web 2.0 - Preliminary Findings. ISMW 1: pp; 517-521.
CrossRef  |  Direct Link  |  

Islam,j and Y. Zhang, 2016. Visual sentiment analysis for social images using transfer learning approach. Visual sentiment analysis for social images using transfer learning approach. 2016 IEEE pp; 124-130.

Kalchbrenner, N., E. Grefenstette and P. Blunsom, 2014. A convolutional neural network for modelling sentences. Master Thesis, Department of Computer Science, University of Oxford, Oxford, England.

Khodayar,M., O. Kaynak and M.E. Khodayar, 2017. Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting. Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting. 2017 Institute of Electrical and Electronics Engineers (IEEE) 2770-2779.

Kumar, R.S., R. Logeswari, N.A. Devi and S.D. Bharathy, 2017. Efficient clustering using ECATCH algorithm to extend network lifetime in wireless sensor networks. IJETT J., 45: 476-481.
CrossRef  |  Direct Link  |  

Kumar, R.S., S. Koperundevi and S. Suganthi, 2016. Enhanced trust based architecture in MANET using AODV protocol to eliminate packet dropping attacks. Int. J. Eng. Trends Technol., 34: 21-27.
CrossRef  |  Direct Link  |  

Kumar, R.S., T. Dhinesh and V. Kathirresh, 2016. Consensus based algorithm to detecting malicious nodes in mobile adhoc network. Int. J. Eng. Res. Technol. (IJERT.), 6: 104-109.
CrossRef  |  Direct Link  |  

Kumar, S.R. and M.G. Abdulla, 2019. Head gesture and voice control wheel chair system using signal processing. Asian J. Inf. Technol., 18: 207-215.
CrossRef  |  Direct Link  |  

Lample,g., M. Ballesteros, S. Subramanian, K. Kawakami and C. Dyer, 2016. Neural architectures for named entity recognition. San Diego, 1: pp; 260-270.
CrossRef  |  Direct Link  |  

Li,C., B. Xu, G. Wu, S. He, G. Tian and H. Hao, 2014. Recursive Deep Learning for Sentiment Analysis over Social Data. Recursive Deep Learning for Sentiment Analysis over Social Data. 2014 IEEE pp; 1381-1429.

Li,W and H. Chen, 2014. Identifying Top Sellers In Underground Economy Using Deep Learning-Based Sentiment Analysis. Identifying Top Sellers In Underground Economy Using Deep Learning-Based Sentiment Analysis. 2014 IEEE pp; 64-67.

Lin,H., J. Jia, J. Qiu, Y. Zhang and G. Shen et al., 2017. Detecting stress based on social interactions in social networks. IEEE Trans. Knowl. Data Eng. 29: 1820-1833.
CrossRef  |  Direct Link  |  

Luo,F., C. Li and Z. Cao, 2016. Affective-Feature-Based Sentiment Analysis Using SVM Classifier. In: Affective-Feature-Based Sentiment Analysis Using SVM Classifier. Luo,F., C. Li and Z. Cao, IEEE China 1.

Majumder,N., S. Poria, A. Gelbukh and E. Cambria, 2017. Deep learning-based document modeling for personality detection from text. IEEE Intell. Syst. 32: 74-79.
CrossRef  |  Direct Link  |  

Mikolov, T., I. Sutskever, K. Chen, G.S. Corrado and J. Dean, 2013. Distributed Representations of Words and Phrases and their Compositionality. In: Advances in Neural Information Processing Systems, Burges, C.J.C., L. Bottou, M. Welling, Z. Ghahramani and K.Q. Weinberger (Eds.). Curran Associates Inc., New York, USA., pp: 3111-3119.

Ouyang,X., P. Zhou, C.H. Li and L. Liu, 2015. Sentiment Analysis Using Convolutional Neural Network. Sentiment Analysis Using Convolutional Neural Network. 2015 IEEE pp; 2359-2364.

P. Vateekul and T. Koomsubha, 2016. A study of sentiment analysis using deep learning techniques on Thai Twitter data. https://ieeexplore.ieee.org/document/7748849

Rodrigues, R.G., R.M.D. Dores, C.G.C. Junior and T.C. Rosa, 2016. SentiHealth-Cancer: A sentiment analysis tool to help detecting mood of patients in online social networks. Int. J. Med. Inf, 85: 80-95.
CrossRef  |  Direct Link  |  

Rosa, R.L., D.Z. Rodriguez and G. Bressan, 2015. Music recommendation system based on user's sentiments extracted from social networks. IEEE Trans. Consum. Electron., 61: 359-367.
CrossRef  |  

Ruangkanokmas,R., T. Achalakul and K. Akkarajitsakul, 2017. Deep Belief Networks with Feature Selection for Sentiment Classification. In: Deep Belief Networks with Feature Selection for Sentiment Classification. Ruangkanokmas,R., T. Achalakul and K. Akkarajitsakul, IEEE Thailand 16.

Sallab,A.A., R. Baly and H. Hajj, 2015. Deep Learning Models for Sentiment Analysis in Arabic. https://www.researchgate.net/publication/280711878_Deep_Learning_Models_for_Sentiment_Analysis_in_Arabic

Sathishkumar, R., K. Kalaiarasan, A. Prabhakaran and M. Aravind, 2019. Detection of lung cancer using SVM classifier and KNN algorithm. Proceedings of the 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), March 29-30, 2019, IEEE, Pondicherry, India, pp: 1-7.

Sathishkumar, R., R. Girivarman, S. Parameswaran and V. Sriram, 2020. Stock price prediction using deep learning and sentimental analysis. Int. J of Emerging Technol. and Innovative Res., 7: 346-354.
Direct Link  |  

Severyn,A and A. Moschitti, 2015. Twitter Sentiment Analysis with Deep Convolutional Neural Networks. Twitter Sentiment Analysis with Deep Convolutional Neural Networks. 2015 ACM pp; 959-962.

Silhavy,R., R. Senkerik, Z. K. Oplatkova, P. Silhavy and Z. Prokopova, 2016. Artificial Intelligence Perspectives in Intelligent Systems. Artificial Intelligence Perspectives in Intelligent Systems. 2016 AISC PP; 249-261.

Singh,j., G. Singh and R. Singh, 2016. A review of sentiment analysis techniques for opinionated web text. CSIT, 4: 241-247.
CrossRef  |  Direct Link  |  

Socher,R., C. C.Y. Lin,A.Y. Ng and C.D. Manning, 2011. Parsing natural scenes and natural language with recursive neural networks. Parsing natural scenes and natural language with recursive neural networks. 2011 United States PP; 129-136.

Socher,R., A. Perelygin, J. Wu, J. Chuang, C.D. Manning, A. Ng and C.Potts, 2013. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. https://aclanthology.org/D13-1170/

Tai,K.S., R. Socher and C.D. Manning, 2015. Improved semantic representations From tree-structured long short-term memory networks. Proc. ACL, 1: pp; 1361-1366.
Direct Link  |  

Tai,K.S., R. Socher and C.D. Manning, 2015. Improved semantic representations From tree-structured long short-term memory networks. Proc. ACL, 1: pp; 1361-1366.
Direct Link  |  

Thapliyal,H., V. Khalus and C. Labrado, 2017. Stress detection and management: A survey of wearable smart health devices. IEEE Consumer Electron. Mag. 6: 64-69.
CrossRef  |  Direct Link  |  

Tsugawa,S., Y. Kikuchi, F. Kishino, K. Nakajima, Y. Itoh and H. Ohsaki, 2015. Recognizing Depression from Twitter Activity. Recognizing Depression from Twitter Activity. 2015 ACM pp; 3187-3196.

W.H. Organization, 2016. World health statistics 2016: Monitoring health forthe sdgs sustainable development goals. https://www.who.int/gho/publications/world_health_statistics/2016/EN_WHS2016_TOC.pdf

Wu,Z., T. Virtanen, T. Kinnunen, E.S. Chng and H. Li, 2021. Exemplar-based unit selection for voice conversion utilizing temporal information. Exemplar-based unit selection for voice conversion utilizing temporal information. 2021 ISCA PP; 3057-3061.

Xue,Y., Qi Li, Li Jin, L.Feng, D.A. Clifton and G.D. Clifford, 2014. Detecting Adolescent Psychological Pressures from Micro-Blog. Detecting Adolescent Psychological Pressures from Micro-Blog. 2014 Springer International Publishing pp;83-94.

Xuezhe. M and E. Hovy, 2016. End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. Assoc. for Comput. Ling, 1: pp;1064-1074.
CrossRef  |  Direct Link  |  

Y. Kim, 2014. Convolutional Neural Networks for Sentence Classification. https://arxiv.org/abs/1408.5882

Yanagimoto,H., M. Shimada and A. Yoshimura, 2013. Document similarity estimation for sentiment analysis using neural network. In: Document similarity estimation for sentiment analysis using neural network. Yanagimoto,H., M. Shimada and A. Yoshimura, IEEE Japan 1.

Yanmei,L and C. Yuda, 2016. Research on Chinese Micro-Blog Sentiment Analysis Based on Deep Learning. In: Research on Chinese Micro-Blog Sentiment Analysis Based on Deep Learning. Yanmei,L and C. Yuda, IEEE China 1.

Yeole,A.V., P.V. Chavan and M.C. Nikose, 2015. Opinion mining for emotions determination. IJACSA, 10.1109/ICIIECS.2015.7192931

You,Q., J. Luo, H. Jin and J. Yang, 2016. Joint Visual-Textual Sentiment Analysis with Deep Neural Networks. Acm Mm, 1: pp; 1071-1074.
CrossRef  |  Direct Link  |  

Zhang,Y., C. Xu, H. Li, K. Yang, J. Zhou and X. Lin, 2018. HealthDep: An efficient and secure deduplication scheme for cloud-assisted health Systems. IEEE Trans. Ind. Inf., 14: 4101-4112.
CrossRef  |  Direct Link  |  

Zhang,Y., J.E. Meng, R. Venkatesan, N. Wang and M. Pratama, 2016. Sentiment Classification using Comprehensive Attention Recurrent Models. In: Sentiment classification using Comprehensive Attention Recurrent models Zhang,Y., J.E. Meng, R. Venkatesan, N. Wang and M. Pratama, IEEE Canada 1.

Zhou, S., Q. Chen and X. Wang, 2013. Active deep learning method for semi-supervised sentiment classification. Neurocomputing, 120: 536-546.
Direct Link  |  

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