Sentiment analysis, a key area in natural language processing, involves determining the sentiment conveyed in textual data. This study investigates the application of logistic regression for sentiment classification across diverse datasets, including social media posts, product reviews, and political opinions, each with unique linguistic characteristics. The process begins with data preprocessing to remove noise and irrelevant content, followed by feature extraction using methods like TF-IDF and word embeddings. These features are then used to train logistic regression models, classifying text samples as positive, negative, or neutral. Model performance is assessed using metrics such as accuracy, precision, recall, and F1-score. The findings demonstrate the effectiveness and practical utility of logistic regression in sentiment analysis, even across varied textual contexts.