Stock Price Prediction utilizing Sentiment Scores Based on Fin-BERT Models

Economic and technological advancements have led to a growing interest in stocks for many people. Investors want to make profits, which has led to the creation of many stock price prediction models using machine learning and deep learning models. Most studies have used news data to create stock price prediction models, but news data tends to be too neutral to accurately classify sentiment. In this study, we aim to create a stock price prediction model using NAVER Stock Discussion Room data, a community data where people of all ages can freely write opinions. In order to more accurately classify sentiment in the financial domain, we used KR-FinBERT, which is trained on Korean financial data, to perform sentiment classification. Since stock prices are most affected by recent data, we used a weighted moving average as a weight to create the final sentiment score. To check whether the sentiment scores generated from the NAVER stock discussion board data have an impact on stock price prediction, we compared the models generated from the data with sentiment scores to the models generated from the data without sentiment scores using three different analysis methodologies. Random Forest, XGBoost, and LSTM. The evaluation metrics used to compare the models were RMSE and MAE. The results of the analysis showed that the evaluation metrics of the data with sentiment scores were better in the model comparison using the three analysis methods, confirming that sentiment scores have an impact on stock price prediction.