The paper describes systems that our team IRLab_DAIICT employed for the shared task Sentiment Analysis for Code-Mixed Social Media Text in SemEval 2020. We conducted our experiments on a Hindi-English CodeMixed Tweet dataset which was annotated with sentiment labels. F1-score was the official evaluation metric and our best approach, an ensemble of Logistic Regression, Random Forest and BERT, achieved an F1-score of 0.693.

The paper describes systems that our team IRLab_DAIICT employed for shared task OffensEval2020: Multilingual Offensive Language Identification in Social Media shared task. We conducted experiments on the English language dataset which contained weakly labelled data. There were three sub-tasks but we only participated in sub-tasks A and B. We employed Machine learning techniques like Logistic Regression, Support Vector Machine, Random Forest and Deep learning techniques like Convolutional Neural Network and BERT. Our best approach achieved a MacroF1 score of 0.91 for sub-task A and 0.64 for sub-task B.