Temporal Dynamics in Financial Sentiment Analysis Using Deep Learning Approaches

This research investigates deep learning techniques for capturing temporal dynamics in financial sentiment analysis. Traditional approaches struggle with the ever-changing nature of financial data. We presented a framework for analyzing sentiment and its evolution in financial text data that employs Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs). Following a survey of the current literature, we offer our methods for sentiment analysis with temporal constraints. This involves data collection, preprocessing, model creation with TensorFlow, and evaluation. We used real-world financial data to validate our strategy. Our deep learning model, specifically tailored for financial tweets, uses LSTMs to take advantage of the sequential nature of text input. We used additional approaches such as batch normalization and token-level sentiment scoring to improve stability and capture fine-grained sentiment. The model performs exceptionally well on both the training and validation datasets, outperforming previous research in accuracy.