Tesla Stock Prediction Based on LSTM and News Sentiment Analysis

DOI

10.2991/978-94-6463-823-3_72How to use a DOI?

Keywords

LSTM; Sentiment Analysis; Stock Prediction

Abstract

The stock market is considered to feature high sensitivity and volatility, with the increasing complexity, stock price prediction is becoming more crucial. As more accurate forecasting can support better decision-making and risk management, investors aim to enhance prediction precision. The integration of news sentiment into traditional predictive models has been perceived as a promising direction. This study sets Tesla Inc. (TSLA) as the research subject to predict its closing price. Sentiment analysis is applied to various sources of news headlines: TSLA financial news, TSLA technology news, and overall market financial news. The corresponding sentiment scores are obtained subsequently. Five LSTM (Long Short-Term Memory) models are constructed using different combinations of news sentiment scores as input features. The obtained results show that the sentiment of news, especially those related to the financial market, can improve model performance. Particularly, when applying all three types of news sentiment, the LSTM model achieves the best performance with an MSE (Mean Squared Error) of 187.72. While technology-related news appears less helpful, even lead to a worse result. This study demonstrates the effectiveness of the incorporation of sentiment analysis into stock price prediction and provides insights for a better understanding of stock market dynamics.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Xinyao Liu
PY  - 2025
DA  - 2025/08/31
TI  - Tesla Stock Prediction Based on LSTM and News Sentiment Analysis
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 734
EP  - 742
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_72
DO  - 10.2991/978-94-6463-823-3_72
ID  - Liu2025
ER  -