A Novel Lstm and Emotion-Driven Multimodal Model for Stock Price Prediction

The goal of our research is to develop a model that achieves higher accuracy in predicting stock price movements, enabling investors to make informed decisions on buying or selling stocks to maximize their benefits. We propose a novel stock price prediction framework that integrates deep learning techniques and sentiment analysis. To achieve more convincing results, we incorporated sentiment data derived from newspaper articles. Using FinBERT (Financial-BERT), we analyzed the sentiments embedded in these articles and incorporated them into our LSTM model. Our model achieved the highest testing accuracy of 65.2 % which is higher than the research results we found. These results highlight the significant role of sentiment in influencing stock price movements and demonstrate the performance of our LSTM-based framework.