A Twitter sentiment based Indian stock price forecasting using deep learning (Record no. 16589)

MARC details
000 -LEADER
fixed length control field 02095nam a22001817a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.312
Item number S225T
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Sanghani, Sureshkumar
245 ## - TITLE STATEMENT
Title A Twitter sentiment based Indian stock price forecasting using deep learning
Statement of responsibility, etc by Sureshkumar Sanghani
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Name of publisher Department of Computer Science and Technology
Year of publication 2023
300 ## - PHYSICAL DESCRIPTION
Number of Pages vii,29p.
Other physical details HB
500 ## - GENERAL NOTE
General note A 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.<br/><br/>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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Department of Computer Science and Technology
Topical Term Stock Index Prediction
Topical Term Sentiment Analysis
Topical Term MTech Theses
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Brahma, Dweepobotee
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
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Withdrawn status Lost status Damaged status Not for loan Collection code Permanent Location Current Location Shelving location Date acquired Source of acquisition Full call number Accession Number Price effective from Koha item type
        Theses S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Reference 2024-04-01 Office of Academics 006.312 S225T TM00525 2024-07-01 Thesis