Sentiment analysis is critical for analyzing public sentiment, especially in the context of political discourse on social media platforms such as Twitter. This study delves into the intricacies of sentiment analysis methodologies within political conversations, employing diverse techniques to draw insights from large datasets. The study covers data preprocessing, encompassing handling missing values, text preprocessing techniques, and data splitting for robust model training and evaluation. Key phases include TF-IDF vectorization for numerical feature representation, label encoding for categorical variables, tokenization for text normalization, and sentiment classification using sentiment analysis tools like VADER and SentimentIntensityAnalyzer from NLTK. The approach combines Min-Max scaling for data standardization, logistic regression for sentiment prediction, and rigorous correlation analysis to determine the links between variables. The results show that the approach is successful, with logistic regression obtaining an impressive 92% accuracy in sentiment identification. The sentiment split displays the percentage of positive tweets (23.20%), negative tweets (7.33%), and neutral tweets (69.46%). Furthermore, a comparison with an alternate dataset using logistic regression revealed a 59% difference in accuracy. The sentiment distribution in this dataset reveals 59.03% of positive tweets, 18.66% of negative tweets, and 22.32% of neutral tweets. This demonstrates whether model performance varies depending on the dataset used. These findings highlight the significance of dataset selection and model assessment in sentiment analysis, demonstrating how various datasets can result in variable accuracy levels and sentiment distributions. Our research focuses on sentiment analysis of huge data from political parties to discover positive, negative, and neutral tweets.