Memes often disseminate misogynistic material, which nurtures gender discrimination and stereotyping. While it is an effective tool of communication, social media has also provided a fertile ground for online abuse. This vital issue in the multilingual and multimodal setting is tackled by the Misogyny Meme Detection Shared Task. Our method employs advanced NLP techniques and machine learning models to classify memes in Malayalam and Tamil, two low-resource languages. Preprocessing of text includes tokenization, lemmatization, and stop word removal. Features are then extracted using TF-IDF. With the best achievable hyperparameters, along with the SVM model, our system provided very promising outcomes and ranked 9th among the systems competing in the Tamil task with a 0.71259 F1-score, and ranked 15th with an F1-score of 0.68186 in the Malayalam taks. With this research work, it would be established how important AI-based solutions are toward stopping online harassment and developing secure online spaces.

Sentiment analysis of code-mixed Dravidian languages has become a major area of concern with increasing volumes of multilingual and code-mixed information across social media. This paper presents the “Seventh Shared Task on Sentiment Analysis in Code-mixed Tamil and Tulu”, which was held as part of DravidianLangTech (NAACL-2025). However, sentiment analysis for code-mixed Dravidian languages has received little attention due to challenges such as class imbalance, small sample size, and the informal nature of the code-mixed text. This study applied an SVM-based approach for the sentiment classification of both Tamil and Tulu languages. The SVM model achieved competitive macro-average F1 scores of 0.54 for Tulu and 0.438 for Tamil, demonstrating that traditional machine learning methods can effectively tackle sentiment categorization in code-mixed languages under low-resource settings.