Analysing Emotional Content in Tweets for Intelligent Music Curation Using Logistic Regression

With the growing influence of social media, understanding user sentiments has become very important for various applications. The aim of this paper is to analyze sentiments in social media posts to detect whether they are positive, negative, or neutral and to provide a unique solution by suggesting music that aligns with the identified sentiment. The proposed system leverages Natural Language Processing (NLP) techniques and Logistic Regression for sentiment classification. The data is obtained from Twitter, where the posts are processed to extract meaningful insights. A web application has been developed using Python, HTML, and CSS to demonstrate the functionality of the model. The application not only predicts the sentiment of the given text but also suggests songs that suit the emotional state of the user, thereby improving the user experience. This paper demonstrates how sentiment analysis can be creatively integrated in daily life to make interactions more engaging and personalized.