Deep Learning for Text-Based Sentiment Prediction

Understanding sentiment in textual data is essential for analyzing user opinions and trends. This study explores a deep learningbased sentiment classification approach using recurrent neural networks (LSTM). The model is trained on the IMDb dataset and achieves an accuracy of 86.0%, outperforming traditional machine learning baselines. Results demonstrate that LSTM effectively captures contextual dependencies, enhancing sentiment analysis capabilities in real-world applications.