Leveraging hybrid model for accurate sentiment analysis of Twitter data

Sentiment analysis has emerged as a vital tool for gauging public opinion in today's fast-paced digital environment. This study examines the use of advanced artificial intelligence techniques to analyze sentiments derived from Twitter, a leading platform for real-time social media engagement. By utilizing Twitter's vast dataset, the research implements a comprehensive pre-processing pipeline that incorporates natural language processing (NLP) techniques such as tokenization, stop-word removal, and stemming to prepare the textual data for analysis. For feature representation, the study employs Bi-Directional Long Short-Term Memory (Bi-LSTM) networks, which are highly effective in identifying sequential patterns within text data. The extracted features are then input into a Logistic Regression model with optimized hyperparameters to classify sentiments as positive or negative. Experimental results highlight the efficacy of this integrated approach, achieving an impressive 81.8% precision, 83.4% recall, 82.5% F1-score, and 82.32% accuracy. These outcomes underscore the strength of combining Bi-LSTM and Logistic Regression for sentiment analysis, offering a robust framework for analyzing unstructured textual data in social media contexts. This approach demonstrates significant potential for enhancing sentiment classification tasks in the ever-evolving digital landscape.