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You have been watching the Telsa stock and are deciding if you should buy some stock before close because you think it will jump up tomorrow, but you want to be more certain about your decision. This project aims to help make that decision. Vader sentiment analysis was implemented on tweets to compute a daily sentiment score. From historical stock data the difference between Tesla opening price and the prior day’s closing price was computed and used as the endogenous variable in an ARIMAX time series model with daily sentiment as an exogenous variable. This final model was able to predict that the Tesla stock will open the next day at a higher price than today’s closing price with 58.8% precision.

Developed and implemented a sentiment analysis model using TensorFlow to predict stock market performance based on Twitter data.

Predicting hourly stock prices using sentiment analysis from Twitter and news headlines with deep learning (LSTM).

Predicting current stock prices with sentiments from Twitter using Spark

Team MCU Capstone Project - Predicting PSEi Stock Movement With Twitter Data Sentiment Analysis

Predicting hourly stock prices using sentiment analysis from Twitter and news headlines with deep learning (LSTM).

this project is focused on predicting stock prices with twitter data, with sentiment analysis, and bag of words methods utilizing TF-IDF.

Predicting Netflix stock prices by combining historical market data with Twitter sentiment analysis. Built using Python, Scikit-learn, and regression models to explore the relationship between public sentiment and market performance.

Predicting Stock Prices Using LSTM, RNN, and XGBoost with Twitter Sentiment Analysis — A comprehensive machine learning project for time-series forecasting that combines deep learning, gradient boosting, and social media insights.