Stock Market Prediction Using Twitter Data Sentiment Analysis

Stock Market Prediction Using Twitter Data Sentiment Analysis

Ankit Madhavi , Dr.Kirti Muley

Abstract – Stock market forecasting is a well-known problem of interest. In today's virtual environment, social media platforms such as Twitter are ideal for expressing public opinion. Using the information accessible on these sites, highly accurate and relevant stock insights can be provided. This study examines various ways for dynamically learning about the market and its tendencies. For this article, three alternative models were employed and ran sentiment analysis on tweets about the company or the stock; the model with the least error is the best and most recommended way for prediction. The results have provided us with a clear polarity picture of the tweets as well as the stocks based on Twitter data, both statistically and graphically.
Keywords – Consumer key, Consumer Secret, Access token, Access token Secret, Long short-term memory (LSTM), Support vector regression (SVR), Random forest regression, Root Mean Squared Error, R-Squared, Twitter, Yahoo Finance,