Trolling refers to any user behaviour on the internet to intentionally provoke or instigate conflict predominantly in social media. This paper aims to classify troll meme captions in Tamil-English code-mixed form. Embeddings are obtained for raw code-mixed text and the translated and transliterated version of the text and their relative performances are compared. Furthermore, this paper compares the performances of 11 different classification algorithms using Accuracy and F1- Score. We conclude that we were able to achieve a weighted F1 score of 0.74 through MuRIL pretrained model.

Code-switching refers to the textual or spoken data containing multiple languages. Application of natural language processing (NLP) tasks like sentiment analysis is a harder problem on code-switched languages due to the irregularities in the sentence structuring and ordering. This paper shows the experiment results of building a Kernel based Extreme Learning Machines(ELM) for sentiment analysis for code-switched Dravidian languages with English. Our results show that ELM performs better than traditional machine learning classifiers on various metrics as well as trains faster than deep learning models. We also show that Polynomial kernels perform better than others in the ELM architecture. We were able to achieve a median AUC of 0.79 with a polynomial kernel.