TSAO: Twitter Sentiment Analysis Using Deep Learning with Optimization Techniques - Kumar, C. Suresh; Kanimozhi, J. K. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Sentiment analysis is essential for understanding people’s attitudes and views about different entities. In this research, proposed Twitter sentiment analysis with optimization (TSAO). Initially the dataset has collected from benchmark datasets. In the data set gathering module, a thorough collection of labeled sentiment data is gathered in order to train and assess the model. The pre-processing module then employs enhanced Part of Speech (POS) labeling with Natural language processing (NLP) and WORDNET to increase the semantic understanding of the text input. To extract relevant features from text for the deep learning training module, a CNN and Dense architecture with Resnet50 are employed. As a result, the model is able to learn sophisticated sentiment patterns and representations. To improve the feature selection method, ensemble feature selection approaches are applied. To find the most relevant and informative features, Elastic Net, Recursive Feature, and Hybrid ML Classifier, which includes the Decision Tree and Random Forest algorithms, are utilized. Optimization approaches are used to fine-tune the model’s parameters and improve its effectiveness using the Alternative Least Square (ALS) algorithm. The Ensemble Stacking and Ensemble Voting algorithms are used in the classification phase to combine predictions from several models and enhance overall classification accuracy.