000			02095nam a22001817a 4500
082			_a006.312 _bS225T
100			_aSanghani, Sureshkumar _945446
245			_aA Twitter sentiment based Indian stock price forecasting using deep learning _cby Sureshkumar Sanghani
260			_aIIT Jodhpur _bDepartment of Computer Science and Technology _c2023
300			_avii,29p. _bHB
500			_aA share market investor is always seeking advice from different investment experts to decide their next investment plan or strategy. Basically, they always look for news about a company’s (in which they are interested) future plans, current financial position, current and future order book, and the impact of local or global scenarios and local or international government policies, etc. They must familiarize themselves with all this information as well as the different opinions of various experts. Twitter is a very good source of such news or expert advice on this topic. Almost all major financial news agencies and stock market experts have their Twitter handles, which publish news and their opinions on the share market based on recent events. We will scrape these tweets and aggregate news about Nifty Bank, Nifty IT, Nifty Energy, and Nifty Automobile stock indexes and do sentiment analysis on this data to calculate the sentiment of each day. We will use stock data of all 4 indexes and use this sentiment value of a given day as one additional variable to train a deep learning model that can predict day-level stock prices at the end of the day. The stock markets are often volatile and change abruptly due to economic conditions, political situations, and major events. Therefore, including the effect of some major events for different top stock indexes is worthwhile for the model to learn the impact and predict the price more accurately.
650			_aDepartment of Computer Science and Technology _945447
650			_aStock Index Prediction _945448
650			_aSentiment Analysis _945449
650			_aMTech Theses _945450
700			_aBrahma, Dweepobotee _945451
942			_cTH
999			_c16589 _d16589