The increasing prevalence of AI-generated content has raised concerns about the authenticity and reliability of online reviews, particularly in resource-limited languages like Tamil and Malayalam. This paper presents an approach to the Shared Task on Detecting AI-generated Product Reviews in Dravidian Languages at NAACL2025, which focuses on distinguishing AI-generated reviews from human-written ones in Tamil and Malayalam. Several transformer-based models, including IndicBERT, RoBERTa, mBERT, and XLM-R, were evaluated, with language-specific BERT models for Tamil and Malayalam demonstrating the best performance. The chosen methodologies were evaluated using Macro Average F1 score. In the rank list released by the organizers, team SSNTrio, achieved ranks of 3rd and 29th for the Malayalam and Tamil datasets with Macro Average F1 Scores of 0.914 and 0.598 respectively.

This paper presents an approach to sentiment analysis for code-mixed Tamil-English and Tulu-English datasets as part of the DravidianLangTech@NAACL 2025 shared task. Sentiment analysis, the process of determining the emotional tone or subjective opinion in text, has become a critical tool in analyzing public sentiment on social media platforms. The approach discussed here uses multilingual BERT (mBERT) fine-tuned on the provided datasets to classify sentiment polarity into various predefined categories: for Tulu, the categories were positive, negative, not_tulu, mixed, and neutral; for Tamil, the categories were positive, negative, unknown, mixed_feelings, and neutral. The mBERT model demonstrates its effectiveness in handling sentiment analysis for codemixed and resource-constrained languages by achieving an F1-score of 0.44 for Tamil, securing the 6th position in the ranklist; and 0.56 for Tulu, ranking 5th in the respective task.

This study focuses on developing a solution for detecting abusive texts on social media against women in Tamil and Malayalam, two low-resource Dravidian languages in South India. As the usage of social media for communication and idea sharing has increased significantly, these platforms are being used to target and victimize women. Hence an automated solution becomes necessary to screen the huge volume of content generated. This work is part of the shared Task on Abusive Tamil and Malayalam Text targeting Women on Social MediaDravidianLangTech@NAACL 2025. The approach used to tackle this problem involves utilizing LLM based techniques for classifying abusive text. The Macro Average F1-Score for the Tamil BERT model was 0.76 securing the 11th position, while the Malayalam BERT model for Malayalam obtained a score of 0.30 and secured the 33rd rank. The proposed solution can be extended further to incorporate other regional languages as well based on similar techniques.

This paper presents the approach and findings from the Multimodal Social Media Data Analysis in Dravidian Languages (MSMDA-DL) shared task at DravidianLangTech@NAACL 2025. The task focuses on detecting multimodal hate speech in Tamil, Malayalam, and Telugu, requiring models to analyze both text and speech components from social media content. The proposed methodology uses language-specific BERT models for the provided text transcripts, followed by multimodal feature extraction techniques, and classification using a Random Forest classifier to enhance performance across the three languages. The models achieved a macro-F1 score of 0.7332 (Rank 1) in Tamil, 0.7511 (Rank 1) in Malayalam, and 0.3758 (Rank 2) in Telugu, demonstrating the effectiveness of the approach in multilingual settings. The models performed well despite the challenges posed by limited resources, highlighting the potential of language-specific BERT models and multimodal techniques in hate speech detection for Dravidian languages.