  Offensive behaviour has become pervasive in the Internet community.
Individuals take the advantage of anonymity in the cyber world and indulge in
offensive communications which they may not consider in the real life.
Governments, online communities, companies etc are investing into prevention of
offensive behaviour content in social media. One of the most effective solution
for tacking this enigmatic problem is the use of computational techniques to
identify offensive content and take action. The current work focuses on
detecting offensive language in English tweets. The dataset used for the
experiment is obtained from SemEval-2019 Task 6 on Identifying and Categorizing
Offensive Language in Social Media (OffensEval). The dataset contains 14,460
annotated English tweets. The present paper provides a comparative analysis and
Random kitchen sink (RKS) based approach for offensive language detection. We
explore the effectiveness of Google sentence encoder, Fasttext, Dynamic mode
decomposition (DMD) based features and Random kitchen sink (RKS) method for
offensive language detection. From the experiments and evaluation we observed
that RKS with fastetxt achieved competing results. The evaluation measures used
are accuracy, precision, recall, f1-score.
