Sentiment Analysis of Indian Political Tweets: A Comparative Study with LSTM and RNN Model

: Sentiment analysis has emerged as one of the prime focuses in machine learning, particularly with the rise of social media platforms such as X (formerly Twitter), Reddit, Instagram, and Facebook. These platforms are now central to public conversations, including discussions on politics, generating massive amounts of data through tweets and comments. The study focuses on applying existing deep learning models to the underexplored domain of sentiment analysis of Indian political tweets. The objective is to determine whether models such as LSTM and RNN are applicable to the analysis of Indian political sentiment. The study uses advanced natural language processing techniques, such as Term Frequency-Inverse Document Frequency (TF-IDF) and Word2Vec, for feature extraction to represent tweet text. The research is testing these models on a new and unique dataset of Indian political tweets to find out which model and feature combination best suits this specific context. Experimental results show that TF-IDF embeddings, along with LSTM and RNN models, significantly outperform Word2Vec in sentiment classification with accuracy rates of 83.02% and 81.06%, respectively. These findings demonstrate the potential of LSTM with TF-IDF to effectively analyze political discourse on social media and suggest insights into the suitability of existing models for Indian political sentiment analysis.