Deep Learning-Based Sentiment Analysis Using Gated Recurrent Unit

Abstract

In today’s volatile financial markets, investor sentiment plays a crucial role in shaping market dynamics and influencing investment decisions. Traditional analytical methods often fail to capture the subtle emotional cues embedded in vast amounts of unstructured textual data derived from news articles, social media, and financial reports. This study addresses this challenge by employing a deep learning-based approach using Gated Recurrent Units (GRU) for sentiment analysis, thereby enhancing the accuracy of financial market predictions. The research employs a systematic methodology that begins with data collection from various financial sources. This is followed by rigorous preprocessing, including data cleaning, tokenization, and downsampling to balance sentiment classes. Sentiment labeling and feature engineering, utilizing word embeddings, convert textual data into a format suitable for deep learning. The Gated Recurrent Unit (GRU) model is then trained on these features, and its performance is evaluated using metrics such as accuracy, precision, recall, and F1-score. Results indicate that while the Gated Recurrent Unit (GRU) model effectively captures neutral sentiments, it struggles to accurately classify negative and positive sentiments, highlighting areas for improvement. These findings underscore the potential of GRU-based models in financial sentiment analysis while emphasizing the need for refined techniques to enhance classification accuracy. Future research should investigate hybrid architectures, integrate attention mechanisms, and leverage real-time data to enhance the robustness and comprehensiveness of market forecasting. These insights strongly advocate for ongoing advancements in deep learning strategies to refine sentiment classification and financial prediction models.