Twitter Sentiment-Based Topic Classification using Machine Learning Algorithms
Vaashini Palaniappan, Aida Mustapha, Rashid Amin, Khuneswari Gopal Pillay, Mohd Shahir Shamsir · Journal of Advanced Research in Applied Sciences and Engineering Technology · 2024
Despite the average of 500 million tweets per day, little research has been conducted to categorize tweets and sentiment polarity so that tweets can be analysed based on user preferences. The objective of this paper is two-fold. The first is to perform comparative experiments for Tweet topic classification using six machine algorithms: Random Forest, K-Nearest Neighbours, Naive Bayes, Logistic Regression, Decision Tree and Support Vector Machine. The second is to investigate the impact of sentiment polarity information in the Tweets data on the topic classification experiment. The model performance is evaluated based on sensitivity, specificity, precision, false positive rate and accuracy. The experimental results showed that the Support Vector Machine (SVM) produced the highest accuracy of 84% in topic classification. After embedding sentiment polarity into the dataset, the accuracy of the topic classification model continued to improve to 93%. In the future, these results can be further enhanced through ensemble machine learning algorithms and potential semantic as well as pragmatic features.