Integrating BERT and LSTM for Enhanced Contextual Understanding Sentimental Analysis on Text

This research explores advanced methods for text sentiment analysis by integrating Bidirectional Encoder Representations from Transformers (BERT) with Long Short-Term Memory (LSTM) networks to enhance the contextual understanding and accuracy of sentiment classification. Traditional sentiment analysis techniques often fail to fully capture the nuances and complexities of human language, especially in informal and short texts like social media posts. Our proposed hybrid model leverages the powerful contextual embeddings generated by BERT, which are then processed by LSTM networks to effectively capture sequential dependencies in the text. Using the Sentiment140 dataset, which contains 1.6 million labeled tweets, we evaluate the performance of this hybrid approach. Preliminary results indicate that this model significantly outperforms conventional machine learning and standalone deep learning models, providing more accurate and context-aware sentiment classifications. This research aims to contribute to the development of more robust sentiment analysis systems capable of handling the intricacies of natural language in social media contexts.