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Kuncahyo Setyo Nugroho
Department of Informatics Engineering, Faculty of Computer Science, Brawijaya University, Indonesia
Computer Science & IT

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Detecting Emotion in Indonesian Tweets: A Term-Weighting Scheme Study Kuncahyo Setyo Nugroho; Fitra A. Bachtiar; Wayan Firdaus Mahmudy
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.61-70

Abstract

Background: Term-weighting plays a key role in detecting emotion in texts. Studies in term-weighting schemes aim to improve short text classification by distinguishing terms accurately. Objective: This study aims to formulate the best term-weighting schemes and discover the relationship between n-gram combinations and different classification algorithms in detecting emotion in Twitter texts. Methods: The data used was the Indonesian Twitter Emotion Dataset, with features generated through different n-gram combinations. Two approaches assign weights to the features. Tests were carried out using ten-fold cross-validation on three classification algorithms. The performance of the model was measured using accuracy and F1 score. Results: The term-weighting schemes with the highest performance are Term Frequency-Inverse Category Frequency (TF-ICF) and Term Frequency-Relevance Frequency (TF-RF). The scheme with a supervised approach performed better than the unsupervised one. However, we did not find a consistent advantage as some of the experiments found that Term Frequency-Inverse Document Frequency (TF-IDF) also performed exceptionally well. The traditional TF-IDF method remains worth considering as a term-weighting scheme. Conclusion: This study provides recommendations for emotion detection in texts. Future studies can benefit from dealing with imbalances in the dataset to provide better performance. Keywords: Emotion Detection, Feature Engineering, Term-Weighting, Text Mining
Co-Authors Fitra Abdurrachman Bachtiar Wayan Firdaus Mahmudy