Emotion Detection on Platform X Comment with Naive Bayes Classification
DOI:
https://doi.org/10.25139/ijair.v7i2.11156Keywords:
Emotion Detection, Naive Bayes Classifier, TF-IDF, X-platform, Natural language processingAbstract
This study aims to develop an effective emotion-detection model for Indonesian-language Twitter comments using a lightweight, interpretable machine learning approach. The proposed method combines the Naive Bayes Classifier (NBC) with Term Frequency–Inverse Document Frequency (TF–IDF) for text feature extraction. The dataset used in this study comprises 3,115 Indonesian-language comments from the publicly available X Emotion Dataset. Emotion detection on Platform X is essential given the platform's high activity and the need for automated monitoring of public sentiment and online behaviour. Four data split scenarios, among them 60:40, 70:30, 80:20, and 90:10, were evaluated to measure the model's accuracy, recall, and precision in classifying emotions into anger, happiness, and sadness. The experimental results show that the 80:20 ratio achieved the highest accuracy of 68.86%, providing an optimal balance between learning efficiency and generalization capability. The anger class consistently achieved the highest recognition rate, while the happy and sad classes showed moderate results due to overlapping linguistic characteristics. Although this study is limited to three emotion classes and a single algorithm, the findings demonstrate that the Naive Bayes–TF–IDF combination remains robust for emotion classification in resource-limited languages. This research contributes an interpretable, computationally efficient framework for social media sentiment analysis and digital behavioural studies in the Indonesian language context.
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