Twitter Sentiment Analysis Using Machine Learning and Deep Learning Techniques

Abstract

This research investigates the use of Machine Learning (ML) and Deep Learning, including BiLSTM approaches, for Sentiment Analysis (SA) of consumer reviews on social media sites. Businesses are increasingly depending on online reviews to determine customer satisfaction due to social media's explosive growth. We used three classification models, the assessment of these attitudes uses Naive Bayes (NB), Support Vector Machine (SVM), and a BiLSTM model. Customer reviews categorized as neutral, negative, or positive feelings made up the dataset used for this study, implying that positive reviews are related to satisfied customers. Text cleaning, tokenization, Bert, and TF-IDF feature extraction were among the preprocessing procedures. Our results show that MNB and CNB had accuracy rates of 80.21 and 81.44%, respectively, whereas linear and RBF SVM had slightly higher accuracy rates of 84.28 and 88.64%, respectively. Also, BiLSTM achieves 87.72%. Besides, this research sheds light on unbalanced dataset issues. Therefore, we apply random oversampling to the research dataset. The evaluated outcomes show that sentiment classification and knowledge extraction through machine learning models and deep learning methods yield beneficial insights that enable businesses to understand their customers better.