The designed framework collects data from tweets and uses natural language processing techniques to extract features. The natural language processing is then applied to classify the sentiment as positive, negative, and neutral. Polarity and partiality are also calculated by the dictionary, which consists of a semantic evaluation of the tweet. It has been observed that natural language processing is a better method for sentiment analysis than traditional methods. There are some limitations in natural language processing, so other machine learning and data mining techniques may be used in the future to address the limitations of these feature vectors and their selection. Future work will focus on a multilingual machine learning algorithm that processes different types of tasks and easily categorizes data into groups and evaluates them based on real-time data opinions