Understanding stock market trends is crucial for investors to make key decisions. It was indeed a wonderful moment when Katla Sai Naveen from the Department of Computer Science and Engineering, presented a paper for the first time with an intelligent solution to that problem. The paper “A Novel Stock Price Prediction Scheme from Twitter Data by using Weighted Sentiment Analysis” was presented at the 12th International Conference on Cloud Computing, Data Science & Engineering (CONFLUENCE-2022), organised by Amity University, Noida, UP, India. It was co-authored by Nikhila Korivi, Godavarthi Chandra Keerthi under the mentorship of Dr V M Manikandan, Assistant Professor, Computer Science and Engineering. The paper will later be published in IEEE Xplore Digital Library.
Abstract:
Stock market forecasting is one of the most interesting research areas for many professionals and researchers. Economic conditions, investor sentiment, current events, future guidance, and a variety of other factors have an impact on the stock market. Since 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, a solution to this problem has been introduced. 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, the impact of the person who wrote the tweet is also considered while predicting the trend. The parameters such as the total number of followers, the emotion of each comment on each post of selected stock, the number of likes and retweets are considered. An overview of the selected stock’s potential will be given to the user as the output.
This research will be useful for businessmen and people with enthusiasm for the stock market. The research will provide insights and intelligence to help make profitable decisions. False positives during sentiment analysis is a major concern in this domain. The team is focused on improving the existing approaches with better methods to identify false positives.