# Stock Price Prediction using Technical Data and Sentiment Score
> Kurniawan A.

URL kanonis: https://discover.unhas.ac.id/publications/stock-price-prediction-using-technical-data-and-sentiment-score
Jurnal / Konferensi: Proceedings of the 2024 IEEE International Conference on Industry 4 0 Artificial Intelligence and Communications Technology Iaict 2024
Tahun terbit: 2024
DOI: https://doi.org/10.1109/IAICT62357.2024.10617768
Citations: 2

## Authors
- Kurniawan A.

## Abstract
This study provides a detailed comparison of machine learning models for stock price prediction using technical and sentiment analysis. Key findings highlight the effectiveness of LSTM with technical data and the benefits of integrating sentiment analysis from BERT with XGBoost for long-term predictions. Sentiment analysis using the modified BERT obtained an accuracy of 92.73%. On the other hand, BERT-LSTM, BERT-XGBoost, and LSTM evaluated using MAE, RMSE, MSE, and MAPE metrics also showed satisfactory results. Although LSTM performs well with technical data alone, combining BERT sentiment analysis with XGBoost shows promise for improving accuracy, especially in long-term predictions. Future research should focus on model refinement and data quality to improve predictive analysis in financial markets. Recommendations are to explore fundamental indicators and refine sentiment analysis by considering user influence.

## Keywords
- Computer science
- Stock price
- Stock (firearms)
- Sentiment analysis
- Artificial intelligence
- Econometrics
- Economics
- Series (stratigraphy)
- Engineering
- Biology
- Mechanical engineering
- Paleontology

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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