Our work addresses the growing concern of abusive comments in online platforms, particularly focusing on the identification of Homophobia and Transphobia in social media comments. The goal is to categorize comments into three classes: Homophobia, Transphobia, and non-anti LGBT+ comments. Utilizing machine learning techniques and a deep learning model, our work involves training on a English dataset with a designated training set and testing on a validation set. This approach aims to contribute to the understanding and detection of Homophobia and Transphobia within the realm of social media interactions. Our team participated in the shared task organized by LTEDI@EACL 2024 and secured seventh rank in the task of Homophobia/Transphobia Detection in social media comments in Tamil with a macro- f1 score of 0.315. Also, our run was submitted for the English language and secured eighth rank with a macro-F1 score of 0.369. The run submitted for Malayalam language securing fourth rank with a macro- F1 score of 0.883 using the Random Forest model.

Our work aims to identify the negative comments that is associated with Counter-speech,Xenophobia, Homophobia,Transphobia, Misandry, Misogyny, None-of-the-above categories, In order to identify these categories from the given dataset, we propose three different models such as traditional machine learning techniques, deep learning model and transfer Learning model called BERT is also used to analyze the texts. In the Tamil dataset, we are training the models with Train dataset and test the models with Validation data. Our Team Participated in the shared task organised by DravidianLangTech and secured 4th rank in the task of abusive comment detection in Tamil with a macro- f1 score of 0.35. Also, our run was submitted for abusive comment detection in code-mixed languages (Tamil-English) and secured 6th rank with a macro-f1 score of 0.42.