Abstract
The advancements in social networking have empowered open expression on micro-blogging platforms like Twitter. Traditional Twitter Sentiment Analysis (TSA) faces challenges due to rule-based or dictionary algorithms, dealing with feature selection, ambiguity, sparse data, and language variations. This study proposed a classification framework for Twitter sentiment data using word count vectorization and machine learning techniques to reduce the difficulties faced with annotated sentiment-labelled tweets. Various classifiers (Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), and Random Forest (RF)) were evaluated based on accuracy, precision, recall, F1-score, and specificity. Random Forest outperformed the others with an Area under Curve (AUC) value of 0.96 and an average precision (AP) score of 0.96 in sentiment classification, especially effective with minimal Twitter-specific features.
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