NEW MODERN APPROACH TO PREDICT USERS SENTIMENT USING CNN AND BLSTM

  • 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

Abstract

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

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Published
2022-12-26
How to Cite
[1]
R. S. Kumar, “NEW MODERN APPROACH TO PREDICT USERS SENTIMENT USING CNN AND BLSTM”, INJIISCOM, vol. 3, no. 2, pp. 41-49, Dec. 2022.