Optimizing Sentiment Analysis on Twitter: Leveraging Hybrid Deep Learning Models for Enhanced Efficiency

Abstract

Sentiment analysis has emerged as a prominent and critical research area, particularly in the realm of social media platforms. Among these platforms, Twitter stands out as a significant channel where users freely express opinions and emotions on diverse topics, making it a goldmine for understanding public sentiment. The study presented in this paper delves into the profound significance of sentiment analysis within the context of Twitter, with a primary focus on uncovering the underlying sentiments and attitudes of users towards various subjects. To achieve it, this study presents a comprehensive analysis of sentiment on Twitter, leveraging a diverse range of advanced deep learning and neural network models, including Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Moreover, investigates the effectiveness of Hybrid Ensemble Models in enhancing sentiment analysis accuracy and optimized time. The proposed architecture (HCCRNN) puts forward a sophisticated deep learning model for sentiment analysis on Twitter data, achieves great accuracy whilst considering computational efficiency. Standard models such as Multinomial-NB, CNN, RNN, RNN-LSTM, and RNN-CNN, as well as hybrid models such as HCCRNN (2CNN-1LSTM), CATBOOST, and STACKING (RF-GBC), were examined CNN and RNN-CNN had the best accuracy (82%) and F1-score (81%), with appropriate precision and recall rates among the conventional models. RNN-CNN surpassed other models in terms of analysis time, requiring just 22.4 min. For hybrid models, our suggested model, HCCRNN (2CNN-1LSTM), attained high accuracy in 59 s and an accuracy of 82.6%. It exhibits the capability of real-time sentiment analysis with extraordinary precision and efficiency. This comprehensive exploration of sentiment analysis on Twitter enriches the knowledge base of the community and the application of sentiment analysis across diverse domains.