Improved semi-supervised learning technique for automatic detection of South African abusive language on Twitter

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Abstract

Semi-supervised learning is a potential solution for improving training data in low-resourced abusive language detection contexts such as South African abusive language detection on Twitter. However, the existing semi-supervised learning methods have been skewed towards small amounts of labelled data, with small feature space. This paper, therefore, presents a semi-supervised learning technique that improves the distribution of training data by assigning labels to unlabelled data based on the majority voting over different feature sets of labelled and unlabelled data clusters. The technique is applied to South African English corpora consisting of labelled and unlabelled abusive tweets. The proposed technique is compared with state-of-the-art self-learning and active learning techniques based on syntactic and semantic features. The performance of these techniques with Logistic Regression, Support Vector Machine and Neural Networks are evaluated. The proposed technique, with accuracy and F1-score of 0.97 and 0.95, respectively, outperforms existing semi-supervised learning techniques. The learning curves show that the training data was used more efficiently by the proposed technique compared to existing techniques. Overall, n-gram syntactic features with a Logistic Regression classifier records the highest performance. The paper concludes that the proposed semi-supervised learning technique effectively detected implicit and explicit South African abusive language on Twitter.CATEGORIES: • Computing computing ~ Natural language processing and sentiment analysis • Computing methodologies ~ Text classification and information extraction

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SMOTE: Synthetic Minority Over-sampling Technique

N. Chawla, K. W. Bowyer, L Hall … (2002)

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.