• Karlapudi Naga Manasa

The rapid increase in usage of Technology has changed the way of expressing people’s opinions, views and Sentiments about specific product, services, people and more, by using social media services such as Facebook, Instagram and Twitter. Due to this is massive amount of data gets generated. To find insights from this Data generated and make certain decision we implement web application that collects twitter data and shows it indifferent statistical forms. The main objective of the work presented with in this paper was to design and implement twitter data analysis and visualization in Python platform. Our primary approach was to focus on real-time analysis rather than historic datasets. Twitter API allow for collecting the sentiments information in the form of either positive score, negative score or neutral. We show the application of sentimental analysis and how to connect to Twitter and run sentimental analysis queries. We run experiments on different queries from politics to humanity and show the interesting results. We realized that the neutral sentiment for tweets are significantly high which clearly shows the limitations of the current works. this study focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, XGBoost Classifier and Support Vector Machine, we provide research on twitter data streams. We have also discussed general challenges and applications of Sentiment Analysis on Twitter.

Twitter, Sentiment analysis (SA), Opinion mining, Machine learning, Naive Bayes (NB), Support Vector Machine (SVM).

"Sentiment analysis of twitter data using Machine learning algorithms ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 10, page no.3770-3776, October-2020, Available :http://www.jetir.org/papers/JETIR2010492.pdf

"Sentiment analysis of twitter data using Machine learning algorithms ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 10, page no. pp3770-3776, October-2020, Available at : http://www.jetir.org/papers/JETIR2010492.pdf