The shared task on Detecting AI-generated Product Reviews in Dravidian Languages is aimed at addressing the growing concern of AI-generated product reviews, specifically in Malayalam and Tamil. As AI tools become more advanced, the ability to distinguish between human-written and AI-generated content has become increasingly crucial, especially in the domain of online reviews where authenticity is essential for consumer decision-making. In our approach, we used the ALBERT, IndicBERT, and Support Vector Machine (SVM) models to classify the reviews. The results of our experiments demonstrate the effectiveness of our methods in detecting AI-generated content.

Sentiment analysis is recognized as an important area in Natural Language Processing (NLP) that aims at understanding and classifying opinions or emotions in text. In the political field, public sentiment is analyzed to gain insight into opinions, address issues, and shape better policies. Social media platforms like Twitter (now X) are widely used to express thoughts and have become a valuable source of real-time political discussions. In this paper, the shared task of Political Multiclass Sentiment Analysis of Tamil tweets is examined, where the objective is to classify tweets into specific sentiment categories. The proposed approach is explained, which involves preprocessing Tamil text, extracting useful features, and applying machine learning and deep learning models for classification. The effectiveness of the methods is demonstrated through experimental results and the challenges encountered while working on the analysis of Tamil political sentiment are discussed.