Impact of SMOTE on Imbalanced Text Features for Toxic Comments Classification Using RVVC Model

Abstract

Social media platforms and microblogging websites have gained accelerated popularity during the past few years. These platforms are used for expressing views and opinions about products, personalities, and events. Often during discussions and debates, fights take place on social media platforms which involves using rude, disrespectful, and hateful comments called toxic comments. The identification of toxic comments has been regarded as an essential element for social media platforms. This study introduces an ensemble approach, called regression vector voting classifier (RVVC), to identify the toxic comments on social media platforms. The ensemble merges the logistic regression and support vector classifier under soft voting criteria. Several experiments are performed on the imbalanced and balanced dataset to analyze the performance of the proposed approach. For data balance, the synthetic minority oversampling technique (SMOTE) is used on the imbalanced dataset. Furthermore, two feature extraction approaches are utilized to investigate their suitability such as term frequency-inverse document frequency (TF-IDF) and bag-of-words (BoW). The performance of the proposed approach is compared with several machine learning classifiers using accuracy, precision, recall, and F1-score. Results suggest that RVVC outperforms all other individual models when TF-IDF features are used with SMOTE balanced dataset and achieves an accuracy of 0.97.

Keywords

• Toxic comments classification
• ensemble classifier
• synthetic minority oversampling technique
• TF-IDF
• BoW
• text classification