A Novel Stock Price Prediction Scheme from Twitter Data by using Weighted Sentiment Analysis

Stock market forecasting has been one of the most interesting subjects for many professionals and researchers to work on as a result of today’s rapid expansion. Economic conditions, investor sentiment, current events, future guidance, and a variety of other factors all have an impact on the stock market. And because the stock market changes swiftly from time to time, it might be tough for a user or investor to keep up with the shifting trend. Combining sentiment analysis with a machine learning model would help with accurate prediction in this case. Where sentiment analysis is a text mining procedure that has one of the most important uses in analysing user reviews and evaluating the overall sentiment of a piece of text. The purpose of this research work is to create a machine learning model that takes recent tweets from the Twitter API and categorises each message as positive, bad, or neutral. Later, depending on the impact and the person who wrote the tweet, a factor will be multiplied. The proposed method would then take into account the post’s owner’s total number of followers, as well as the emotion of each comment on each post of selected stock, as well as the number of likes and retweets, utilising market indicators and pricing. The user would be given an overview of the selected stock’s potential.