Twitter sentiment Analysis Using Machine Learning

abstract on page 1 With the development and expansion of web technology, a vast amount of data is generated and available to internet users, and the internet has evolved into a platform for online learning, idea exchange, and opinion sharing. Because they enable people to share and express their opinions on various topics, engage in discussions with various communities, or post messages globally, social networking sites like Facebook, Google, and Twitter are quickly becoming more and more popular. A lot of work has been done in the field of sentiment analysis of Twitter data, which is useful for analyzing the information in tweets where opinions are highly unstructured and heterogeneous and are either This paper presents a survey and comparative analyses of current techniques for opinion mining, such as machine learning and lexicon-based approaches, along with evaluation metrics using various machine learning algorithms, such as naive bayes max entropy and support vector machine. Given the growth and advancement of web technology, there is a huge volume of data available on the internet for internet users, and a lot of data is generated too. In some cases, this data is neutral, positive, or negative. We offer data stream research on Twitter. Additionally, we talked about the basic difficulties and uses of sentiment analysis on Twitter