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In a day and age where billions of users regularly use social media and express their opinions online, there is potentially a lot of data that can be harvested and utilized; therefore, it is crucial to develop a quick way to garner data. This study aimed to develop a program using Python to do the same, trying to understand the sentiments of the authors of the text and tweets as well as other additional information about the top tweets and retweets. The main objective of the Twitter Sentiment Analysis is a query-based analysis of tweets. In simple words, Twitter Sentiment Analysis focuses on analyzing the tweets of a specific/particular topic that the user wants to analyze. An extensive collection of such sentiments could leverage to provide a fair reflection of public sentiment towards a specific topic. There are thousands of tweets that can be quickly processed for the sentimental impact, compared to the amount of time it would take a large team of people to complete the same task manually. There are tons of text documents that can also be processed for sentiments in seconds, much faster than just a team of people manually skimming through the text.

Sentiment Analysis of Tweets and Texts Using Python on Stocks and COVID-19