TWITTER EMOTION ANALYTICS FOR BUSINESS INTELLIGENCE: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS
DOI:
https://doi.org/10.69980/vdfhmz46Keywords:
Emotion Classification, Social Media Analytics, Business Intelligence, Sentiment Analysis, Decision Support SystemsAbstract
Text-to-emotion detection in social media has become a vital activity in terms of the sentiment and mental health trends of people, as a tool for business intelligence and decision support. This paper reinstates and expands the deep learning-based sentiment classification framework, initially proposed to analyse Twitter (now X) emotion in terms of the number of emotions: anger (0), joy (1), fear (2), love (3), sadness (4), and surprise (5), with the help of a Multi-Layer Perceptron (MLP) classifier on a large-scale dataset with six emotions. After completing the preprocessing pipeline provided in the referenced study (tokenisation, stop-word removal, and POS-aware lemmatisation), the cleaned text is vectorised using TF-IDF with a vocabulary count of 5,000 features. Although the baseline MLP architecture achieved 85.25% test accuracy (better than the original study), a systematic comparative analysis shows that simple classical models outperform neural approaches for this task. Logistic Regression, trained on the same TF-IDF representation, achieved 89.09% accuracy through 5-fold stratified cross-validation, with improved generalisation and better performance on minority classes such as fear and surprise. The Multinomial Naive Bayes model achieved 83.19% mean cross-validated accuracy. These results offer practical advantages for real-time analytics in organisational settings. The findings challenge the assumption that complex deep learning frameworks are inherently superior for short-text emotion classification using lexical features. Logistic Regression demonstrates higher accuracy, interpretability, and computational efficiency on this dataset of over 416,000 instances, supporting managerial decision-making and social media-based business insights.
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