This project integrates Unsupervised Machine Learning through clustering techniques and Naive Bayes Classification to perform sentiment analysis on Twitter data. Leveraging Text Mining methodologies, the system extracts valuable insights from the vast amount of textual content on the platform. The use of clustering algorithms helps identify patterns and group similar tweets together, facilitating a deeper understanding of the overall sentiment landscape.

The core of the sentiment analysis involves employing Naive Bayes Classification, a machine learning algorithm adept at categorizing tweets based on sentiment (positive, negative, or neutral). This classification adds a layer of accuracy to the sentiment analysis process, enabling a nuanced understanding of the sentiments expressed in Twitter data.

To enhance the interpretability and accessibility of the findings, the project incorporates Visualization techniques. Through graphical representations and visual analytics, the sentiment trends and clusters can be comprehensively presented. This visualization aspect contributes to a user-friendly interface, making it easier for stakeholders to grasp and interpret the sentiment dynamics within the Twitter data.