Offensive Language Detection Using Text Classification

Abstract

There is a concerning rise of offensive language on the content generated by the
various social platforms. Such language might bully
or hurt the feelings of an individual or a
community. Recently, the research community has
investigated and developed different supervised
approaches and training datasets to detect or
prevent offensive monologues or dialogues
automatically. In this study, we propose a model for
text classification consisting of modular cleaning
phase and tokenizer, three embedding methods, and
eight classifiers. Our experiments show a promising
result for detection of offensive language on our
dataset obtained from Twitter.
Considering hyperparameter optimization, three
methods of AdaBoost, SVM and MLP had highest
average of F1-score on popular embedding method
of TF-IDF. Index Terms— offensive language
detection, social media, machine learning, text
mining. This paper reviews text classification
methods for offensive language detection in online
platforms. It covers algorithms like Naive Bayes,
SVMs, and neural networks, along with feature
engineering techniques and evaluation metrics.
Insights into current research and future directions
are provided.