With technology's increasing capabilities, social media has become the largest pool of data from which it can extract public opinion and begin to gather informative data on the success or failure of a brand, product, or marketing campaign in the eyes of the public. People share their experiences, opinions, and daily activities on social media, which results in enormous amounts of online data that attract developers to carry out data mining and analysis. Thus, there is a necessity for social media screening to obtain results that can be used for analysis. Twitter is an online networking site driven by tweets, which are 140-character limited messages. Thus, the character limit enforces the use of hashtags for text classification. Currently, around 5500–6000, tweets are published every second, which results in approximately 561.6 million tweets per day. Performing sentiment analysis of tweets can help us to determine the polarity and inclination of a vast population toward a specific topic, term, or entity. The applications of such analysis can easily be observed during public elections, movie promotions, brand endorsements, and many other fields. This proposed system uses a Naïve Bayes classifier to determine the tweets based on sentiment. In the implemented system, tweets are collected, and sentiment analysis is performed on them. Based on the sentiment analysis results, a few suggestions can be provided to the user. The primary aim is to provide a method for analyzing sentiment scores based on grades. This paper reports on the design of sentiment analysis, extracting vast numbers of tweets. Results classify users' perceptions via tweets into positive and negative categories. Secondly, it discusses various techniques to carry out a sentiment analysis on Twitter data in detail.