Sentiment Analysis using Machine learning for forecasting Indian stock Trend: A brief Survey

Abstract

Due to new technical advances, the machine can think as a person-investor and express its reaction to readily available financial information. Forecasting models for the Indian stock market can be developed based on the analysis of these sentiments. The purpose of the study is to identify gaps in existing approaches to the analysis of sentiments and models of forecasting trends in the Indian stock market, which can improve the accuracy of the prediction of the dynamics of Indian stocks. The paper presents an overview of the literature on the analysis of sentiments of financial information using lexical methods, machine learning methods and forecasting for the Indian stock market based on sentiment analysis data. The scientific works, conference reports, dissertations, books and articles published by scientists for the period from 2015 to 2021 are considered. The datasets published in Indian Stock Exchanges suggest increasing participation of retail investors in the Indian Stock market in recent times. To help investors in decisionmaking, various prediction models are available based on the financial information. The results of the survey showed that investors’ attitudes based on the microeconomic and macroeconomic information associated with stocks influence the movement of the stock price. Therefore, forecasting a future trend or price requires a sentiments analysis based on available financial information. It was concluded that using machine learning to extract sentiment from financial data allows for more accurate forecasts than sentiment analysis based on vocabulary. The results of this study can be useful for students and new professionals in the field of financial information data analysis and stock market predictions who want to get connected with this area, identify problem concerns, and develop models for predicting decision-making.

Keywords

• sentiment analysis
• stock market
• prediction
• machine learning
• decision making
• trend analysis