To Enhance the Impact of Deep Learning-Based Algorithms in Determining the Behavior of an Individual based on Communication on Social Media
Sunayana Shivthare1, Yogesh Kumar Sharma2, Ranjit D. Patil3

1Mrs. Sunayana Shivthare *, Assistant Professor, Department of Computer Science, Dr. D. Y. Patil ACS College, Pimpri, Pune, India.
2Dr. Yogesh Kumar Sharma, Associate Professor, and Research Coordinator, Department of Computer Science and Engineering, Shri JJT University, Jhunjhunu, Rajasthan.
3Dr. Ranjit D. Patil, Vice-Principal, and H.O.D, Dr. D.Y. Patil ACS College, Pimpri, Pune, India.

Manuscript received on September 17, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4433-4435 | Volume-8 Issue-12, October 2019. | Retrieval Number: L38411081219/2019©BEIESP | DOI: 10.35940/ijitee.L3841.1081219
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Abstract: In this digitized world, the Internet has become a prominent source to glean various kinds of information. In today’s scenario, people prefer virtual reality instead of one to one communication. The Majority of the population prefers social networking sites to voice themselves through posts, blogs, comments, likes, dislikes. Their sentiments can be found/traced using opinion mining or Sentiment analysis. Sentiment analysis of social media text is a useful technique for identifying peoples’ positive, negative or neutral emotions/sentiments/opinions. Sentiment analysis has gained special attention by researchers from last few years. Traditionally many machine learning algorithms were used to implement it like navie bays, Support Vector Machine and many more. But to overcome the drawbacks of ML in terms of complex classification algorithms different deep learning-based algorithms are introduced like CNN, RNN, and HNN. In this paper, we have studied different deep learning algorithms and intended to propose a deep learning-based model to analyze the behavior of an individual using social media text. Results given by the proposed model can utilize in a range of different fields like business, education, industry, politics, psychology, security, etc.
Keywords: Deep learning, Machine Learning, Social Media, Sentiment analysis.
Scope of the Article: Machine Learning