Easy Data Augmentation in Sentiment Analysis of Cyberbullying
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Abstract (arXiv)
Instagram, a social media platform, has in the vicinity of 2 billion active users in 2023. The platform allows users to post photos and videos with one another. However, cyberbullying remains a significant problem for about 50% of young Indonesians. To address this issue, sentiment analysis for comment filtering uses a Support Vector Machine (SVM) and Easy Data Augmentation (EDA). EDA will augment the dataset, enabling robust prediction and analysis of cyberbullying by introducing more variation. Based on the tests, SVM combination with EDA results in a 2.52% increase in the k-Fold Cross Validation score. Our proposed approach shows an improved accuracy of 92.5%, 2.5% higher than that of the existing state-of-the-art method. To maintain the reproducibility and replicability of this research, the source code can be accessed at uns.id/eda_svm.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 19 references that are in the map. First posted on arXiv 29 Nov 2023.
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4 of 19 references.
References · 4
- 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
- 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
- 2020A Close Look at Deep Learning with Small Datacited by 54
- 2021Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment Analysiscited by 10
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