Tesla Stock Prediction Based on Twitter Comments’ Sentiment Feature Extraction and Ensemble Networks

Abstract

As people become more aware of environmental issues, Tesla, a pillar of the global electric vehicle market, has become a hot commodity in recent years. The prediction of Tesla’s share price is also a hot topic in the investment market. This experiment extracts sentiment factors of tweets about Tesla’s comments, combined with Tesla’s historical stock price as the dataset for training, testing and inspection. This experiment builds time series prediction models based on LSTM, XGBoost and RF algorithms to predict Tesla’s stock price, and evaluates the prediction effectiveness of the three algorithms based on the fit and error of the prediction results. The analysis of the data shows that XGBoost has the best fit and the lowest error among the three algorithms, and that the sentiment factor has its unique utility as raw data. The experimental results also empirically demonstrate the applicability of sentiment factor analysis and the three algorithms LSTM, XGBoost and RF in the field of stock price prediction.