Abstract
With a focus on user engagement, content distribution, sentiment analysis, and predictive modeling, the study provides a thorough analysis of Twitter data. Using popular hashtags, tweet sources, and user locations, the analysis starts by visualizing the data using Python libraries like Plotly, Seaborn, and WordCloud. To understand user behavior patterns and extract temporal information, exploratory data analysis techniques are used, and furthermore the dominant sentiment in the dataset, sentiment analysis is also carried out. The research goes one step further and involves training a neural network for classification tasks through machine learning modeling. The outcomes show how to visualize sentiment trends, tweet content, and model performance in an insightful way. The results provide insightful information about sentiment patterns, user interactions, and the dynamics of content dissemination on the Twitter network.