Analyzing Impact of Social Media Sentiments on Financial Markets
Poorna Chandra Vemula1, Santhosh Reddy Chilaka2, Mullapudi Raghu Vamsi3, Jonnalagadda Praveen Reddy4, Venkata Sai Mahendra Somineni5

1Poorna Chandra Vemula*, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2Santhosh Reddy Chilaka, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
3Mullapudi Raghu Vamsi, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
4Jonnalagadda Praveen Reddy, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
5Venkata Sai Mahendra Somineni, Department of Computer Science, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on August 05, 2021. | Revised Manuscript received on August 25, 2021. | Manuscript published on August 30, 2021. | PP: 113-120 | Volume-10, Issue-10, August 2021 | Retrieval Number: : 100.1/ijitee.J941108101021 | DOI: 10.35940/ijitee.J9411.08101021
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper analyzes the impact of continuously changing sentiments on apparently unstable stock exchange. Right when a monetary supporter decides to buy or sell stock, his decision is very much dependent on to rise or fall in price of the stock. In this paper, we look at the possibility of using notion attitudes (good versus negative) and moreover sentiments (delight, feel sorry for, etc) isolated from finance related news or tweets to help predict stock worth turns of events. This examination uses a model-self-ruling approach to manage uncover the mysterious components of stock exchange data using distinctive significant learning techniques like Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU).
Keywords: Stock Exchanges, Tweets, Sentiments, Recurrent Neural Networks (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU).