• R. Sathish Kumar Department of Computer Science and Engineering Manakula Vinayagar Institute of Technology, Kalitheerthalkuppam, Puducherry, India
Keywords: Sentimental Analysis, Recommendation System, Deep Learning, CNN, BLSTM, Social Networks


In Today’s world social network play a vital role and
provides relevant information on user opinion. This
paper presents emotional health monitoring system to
detect stress and the user mood. Depending on results
the system will send happy, calm, relaxing or
motivational messages to users with phycological
disturbance. It also sends warning messages to
authorized persons in case a depression disturbance is
detected by monitoring system. This detection of
sentence is performed through convolution neural
network (CNN) and bi-directional long-term memory
(BLSTM). This method reaches accuracy of 0.80 to detect
depressed and stress users and also system consumes
low memory, process and energy. We can do the future
work of this project by also including the sarcastic
sentences in the dataset. We can also predict the
sarcastic data with the proposed algorithm


Al-Qurishi, M., Hossain, M. S., Alrubaian, M., Rahman, S. M. M., & Alamri, A. (2017).
Leveraging analysis of user behavior to identify malicious activities in largescale social networks. IEEE Transactions on Industrial Informatics, 14(2), 799-
Araque, O., Corcuera-Platas, I., Sánchez-Rada, J. F., & Iglesias, C. A. (2017). Enhancing
deep learning sentiment analysis with ensemble techniques in social
applications. Expert Systems with Applications, 77, 236-246.
Berbano, A. E. U., Pengson, H. N. V., Razon, C. G. V., Tungcul, K. C. G., & Prado, S.
V. (2017, September). Classification of stress into emotional, mental, physical
and no stress using electroencephalogram signal analysis. In 2017 IEEE
International Conference on Signal and Image Processing Applications
(ICSIPA) (pp. 11-14). IEEE.
Glavan, I. R., Mirica, A., & Firtescu, B. N. (2016). The Use of Social Media for
Communication In Official Statistics at European Level. Romanian Statistical
Review, 64(4), 37-48.
Guimaraes, R. G., Rosa, R. L., De Gaetano, D., Rodriguez, D. Z., & Bressan, G. (2017).
Age groups classification in social network using deep learning. IEEE
Access, 5, 10805-10816.
Huang, Y. P., Goh, T., & Liew, C. L. (2007, December). Hunting suicide notes in web
2.0-preliminary findings. In Ninth IEEE International Symposium on
Multimedia Workshops (ISMW 2007) (pp. 517-521). IEEE.
Khodayar, M., Kaynak, O., & Khodayar, M. E. (2017). Rough deep neural architecture
for short-term wind speed forecasting. IEEE Transactions on Industrial
Informatics, 13(6), 2770-2779.
Kumar, R. S., & Pariselvam, S. (2012). Formative impact of Gauss Markov mobility
model on data availability in MANET‖. Asian Journal of Information
Technology, 11(3), 108-116.
Kumar, R. S., Dhinesh, T., & Kathirresh, V. Consensus Based Algorithm to Detecting
Malicious Nodes in Mobile Adhoc Network. International Journal of
Engineering Research & Technology (IJERT) Vol, 6.
Kumar, R. S., Koperundevi, S., & Suganthi, S. (2016). Enhanced Trust Based
Architecture in MANET using AODV Protocol to Eliminate Packet
Dropping Attacks. International Journal of Engineering Trends and
Technology, 34, 21-27.
Kumar, R., Logeswari, R., Devi, N., & Bharathy, S. (2017). Efficient clustering using
ECATCH algorithm to extend network lifetime in wireless sensor
networks. Int. J. Eng. Trends Technol, 45, 476-481.
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016). Neural
architectures for named entity recognition. arXiv preprint arXiv:1603.01360.
Lin, H., Jia, J., Qiu, J., Zhang, Y., Shen, G., Xie, L., ... & Chua, T. S. (2017). Detecting
stress based on social interactions in social networks. IEEE Transactions on
Knowledge and Data Engineering, 29(9), 1820-1833.
Ma, X., & Hovy, E. (2016). End-to-end sequence labeling via bi-directional lstm-cnnscrf. arXiv preprint arXiv:1603.01354.
Majumder, N., Poria, S., Gelbukh, A., & Cambria, E. (2017). Deep learning-based
document modeling for personality detection from text. IEEE Intelligent
Systems, 32(2), 74-79.
Rodrigues, R. G., das Dores, R. M., Camilo-Junior, C. G., & Rosa, T. C. (2016).
SentiHealth-Cancer: a sentiment analysis tool to help detecting mood of
patients in online social networks. International journal of medical
informatics, 85(1), 80-95.
Rosa, R. L., Rodriguez, D. Z., & Bressan, G. (2015). Music recommendation system
based on user's sentiments extracted from social networks. IEEE Transactions
on Consumer Electronics, 61(3), 359-367.
Sathish Kumar R, Abdulla M.G. (2019). “Head Gesture and Voice Control Wheel Chair
System using Signal Processing”, Asian Journal of Information Technology,
18(18), Issue 8, 1682-3915.
Sathish Kumar R, Girivarman R, Parameshwaran S, Sriram V. (2020) "STOCK PRICE
ANALYSIS", International Journal of Emerging Technologies and Innovative
Research 7(5), 346-354.
Sathishkumar, R., Kalaiarasan, K., Prabhakaran, A., & Aravind, M. (2019, March).
Detection of lung cancer using SVM classifier and KNN algorithm. In 2019
IEEE International Conference on System, Computation, Automation and
Networking (ICSCAN) (pp. 1-7). IEEE.
Thapliyal, H., Khalus, V., & Labrado, C. (2017). Stress detection and management: A
survey of wearable smart health devices. IEEE Consumer Electronics
Magazine, 6(4), 64-69.
Tsugawa, S., Kikuchi, Y., Kishino, F., Nakajima, K., Itoh, Y., & Ohsaki, H. (2015, April).
Recognizing depression from twitter activity. In Proceedings of the 33rd annual
ACM conference on human factors in computing systems (pp. 3187-3196).
World Health Organization. (2016). World health statistics 2016: monitoring health for the
SDGs sustainable development goals. World Health Organization.
Xue, Y., Li, Q., Jin, L., Feng, L., Clifton, D. A., & Clifford, G. D. (2014, April). Detecting
adolescent psychological pressures from micro-blog. In International
Conference on Health Information Science (pp. 83-94). Springer, Cham.
Zhang, Y., Xu, C., Li, H., Yang, K., Zhou, J., & Lin, X. (2018). HealthDep: An efficient
and secure deduplication scheme for cloud-assisted eHealth systems. IEEE
Transactions on Industrial Informatics, 14(9), 4101-4112
How to Cite