Bi-LSTM model for Sentimental Analysis for Recommendation and Classification on X Dataset

In the era of rapid digital communication, social media platforms such as X offer valuable insights into public sentiment on various topics, including products and services. This project leverages a Bidirectional Long Short-Term Memory (Bi-LSTM) model integrated with sentiment analysis to classify and recommend products based on user sentiments from X datasets. By analyzing tweets and categorizing them as positive, negative, or neutral, the system enhances the accuracy of sentiment-driven recommendations. This model addresses challenges like informal language and contextual nuances, which traditional models often overlook. Through rigorous data preprocessing, Bi-LSTM’s ability to capture bidirectional dependencies, and performance evaluation using metrics like accuracy, precision, recall, and F1 score, our approach demonstrates significant improvements in sentiment classification accuracy. The developed system is not only capable of predicting sentiments but also providing actionable recommendations, making it a valuable tool for businesses and researchers.