Algorithmic Trading and Sentiment Analysis in Indian Stock Market - Patil Smita Satish; Kubsad Pramod; Kulkarni Savitha (ITM Web of Conferences)
The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Sentiment analysis, an important part of text mining, attempts to learn about the author’s opinions on a text through its content and structure. Such information is particularly valuable for determining the overall opinion of a large number of people. Examples of its usefulness are predicting box office sales or stock prices. One of the most accessible sources of user-generated data is Twitter, which makes the majority of its user data freely available through its data access API. This study, will predict a sentiment value for stock-related tweets on Twitter, and demonstrate a correlation between this sentiment and the movement of a company’s stock price in a real-time streaming environment. This study data ranges from the period 2018 to 2024. The study reveals that the percentage of error which is less than 5% on almost all companies except one. Where it tells that if the percentage of Error is less than 5 then the accuracy is high and the predicted prices are more accurate.