Twitter sentiment Analysis Using Machine Learning

Twitter sentiment Analysis Using Machine Learning

Ashwini S.Domade 1, Snehal K.Gadakh2, Vaishnavi G.Kardile 3 , Damini S.Pekhale 4

1Ashwini Domade Department of Information and Technology from Matoshri Aasrabai Polytechnic

2Snehal Gadakh Department of Information and Technology from Matoshri Aasrabai Polytechnic

3Vaishnavi Kardile Department of Information and Technology from Matoshri Aasrabai Polytechnic

4Damini Pekhale Department of Information and Technology from Matoshri Aasrabai Polytechnic

5Ms.Vidya Kale lecturer of Information Technology from Matoshri Aasrabai Polytechnic

6Mr.Mahesh Bhandakkar Head of Information Technology from Matoshri Aasrabai Polytechnic

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Abstract - 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 keywords. Opinion mining machine learning using sentiment analysis on Twitter Maximum Entropy Support Vector Machine (SVM) naïve Bayes NB

    Key Words: Twitter, Sentiment analysis (SA), Opinion mining, Machine learning, Naive Bayes (NB), Maximum Entropy,   Support Vector Machine (SVM).