The rise of misogynistic memes on social media posed challenges to civil discourse. This paper aimed to detect misogyny in Dravidian language memes using a multimodal deep learning approach. We integrated Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), EfficientNet, and a Vision Language Model (VLM) to analyze textual and visual informa tion. EfficientNet extracted image features, LSTM captured sequential text patterns, and BERT learned language-specific embeddings. Among these, VLM achieved the highest accuracy of 85.0% and an F1-score of 70.8, effectively capturing visual-textual relationships. Validated on a curated dataset, our method outperformed baselines in precision, recall, and F1-score. Our approach ranked 12th out of 118 participants for the Tamil language, highlighting its competitive performance. This research emphasizes the importance of multimodal models in detecting harmful content. Future work can explore improved feature fusion techniques to enhance classification accuracy.

There is opportunity for machine learning and natural language processing research because of the growing volume of textual data. Although there has been little research done on trend extraction from YouTube comments, sentiment analysis is an intriguing issue because of the poor consistency and quality of the material found there. The purpose of this work is to use machine learning techniques and algorithms to do sentiment analysis on YouTube comments pertaining to popular themes. The findings demonstrate that sentiment analysis is capable of giving a clear picture of how actual events affect public opinion. This study aims to make it easier for academics to find high-quality sentiment analysis research publications. Data normalisation methods are used to clean an annotated corpus of 1500 citation sentences for the study. .For classification, a system utilising one machine learning algorithm—K-Nearest Neighbour (KNN), Na ̈ıve Bayes, SVC (Support Vector Machine), and RandomForest—is built. Metrics like the f1-score and correctness score are used to assess the correctness of the system.