Sentiment Analysis of Modern Literature Using BiLSTM and GloVe Embeddings Using NLP

Sentiment analysis is a key task in Natural Language Processing (NLP) that involves classifying emotions, opinions, and sentiments from text. Traditional machine learning models such as Naïve Bayes, Support Vector Machines (SVM), and Random Forest have been commonly used for this purpose, but they struggle to capture the complex sequential dependencies within text. This study explores the use of Bi-directional Long Short-Term Memory (BiLSTM) networks, combined with pre-trained GloVe embeddings, for sentiment classification. The performance of the BiLSTM model is compared to traditional models and the transformer- based BERT model to assess its computational efficiency and accuracy. While BERT outperforms BiLSTM in accuracy, the BiLSTM model offers competitive performance with greater computational efficiency. This research lays the groundwork for future improvements in sentiment analysis, particularly in real-time applications and multilingual data processing.