Social media abuse of women is a widespread problem, especially in regional languages like Tamil and Malayalam, where there are few tools for automated identification. The use of machine learning methods to detect abusive messages in several languages is examined in this work. An external dataset was used to train a Support Vector Machine (SVM) model for Tamil, which produced an F1 score of 0.6196. Using the given dataset, a Multinomial Naive Bayes (MNB) model was trained for Malayalam, obtaining an F1 score of 0.6484. Both models processed and analyzed textual input efficiently by using TF-IDF vectorization for feature extraction. This method shows the ability to solve the linguistic diversity and complexity of abusive language identification by utilizing language-specific datasets and customized algorithms. The results highlight how crucial it is to use focused machine learning techniques to make online spaces safer for women, especially when speaking minority languages.

Sentiment analysis in code-mixed social media comments written in Tamil and Tulu presents unique challenges due to grammatical inconsistencies, code-switching, and the use of non-native scripts. To address these complexities, we employ pre-processing techniques for text cleaning and evaluate machine learning models tailored for sentiment detection. Traditional machine learning methods combined with feature extraction strategies, such as TF- IDF, are utilized. While logistic regression demonstrated reasonable performance on the Tamil dataset, achieving a macro F1 score of 0.44, support vector machines (SVM) outperformed logistic regression on the Tulu dataset with a macro F1 score of 0.54. These results demonstrate the effectiveness of traditional approaches, particularly SVM, in handling low- resource, multilingual data, while also high- lighting the need for further refinement to improve performance across underrepresented sentiment classes.