Sentiment Analysis and Homophobia detection of Code-Mixed Dravidian Languages leveraging pre-trained model and word-level language tag

Abstract

Social media platforms have seen a significant rise in user engagement in recent years. More and more people are expressing their views and ideas on social platforms. There is an ardent need to develop an accurate system to classify text based on sentiments. In this paper, our team IRLab@ IITBHU presents a solution architecture submitted to the shared task “Sentiment Analysis and Homophobia Detection of YouTube Comments in Code-Mixed Dravidian Languages" organized by DravidianCodeMix 2022 at Forum for Information Retrieval Evaluation (FIRE) 2022. to reveal how sentiment is expressed in code-mixed scenarios. For task A, we used mBERT model and word-level language tag to classify YouTube comments into positive, negative, neutral, or mixed emotions. And for Task B, we performed basic preprocessing steps and built mBERT model to identify homophobia, transphobia, and non-anti-LGBT+ content from the given corpus. For Task A, our proposed system achieved the best result, securing the first rank for Malayalam-English and Kannada-English code-mixed datasets with the 𝐹1 score of 0.72 and 0.66 respectively.

Supriya Chanda

Research Scholar (2018-2024)

Supriya Chanda (pronounced as Supriyo), completed his Ph.D in the Department of Computer Science and Engineering, IIT (BHU), Varanasi. He did his research under the guidance of Dr. Sukomal Pal at the Information retrieval lab.