PANDAS@Abusive Comment Detection in Tamil Code-Mixed Data Using Custom Embeddings with LaBSE
Krithika Swaminathan, K Divyasri, G L Gayathri, D. Thenmozhi, Bharathi B · 2022
Abusive language has lately been prevalent in comments on various social media platforms.The increasing hostility observed on the internet calls for the creation of a system that can identify and flag such acerbic content, to prevent conflict and mental distress.This task becomes more challenging when low-resource languages like Tamil, as well as the oftenobserved Tamil-English code-mixed text, are involved.The approach used in this paper for the classification model includes different methods of feature extraction and the use of traditional classifiers.We propose a novel method of combining language-agnostic sentence embeddings with the TF-IDF vector representation that uses a curated corpus of words as vocabulary, to create a custom embedding, which is then passed to an SVM classifier.Our experimentation yielded an accuracy of 52% and a macro F1-score of 0.54.