• Rahul Darshan Harsha
• Anjan Girish

Social networking services like Twitter, Facebook, Tumbler, and others play a significant part in today's society. Twitter is a microblogging site that offers a tonne of data that may be utilized for many Sentiment Analysis applications, including forecasts, reviews, elections, marketing, etc. Sentiment Analysis is a method of taking information from a lot of data and categorizing it into several groups known as sentiments. Python is a well-known high-level, interpreted, dynamic programming language that is straightforward but incredibly effective. It uses the NLTK (Natural Language Toolkit) to analyze data from natural languages. Python's NLTK library is a foundation for creating programs and categorizing data. Additionally, NLTK offers graphical examples of different outcomes or trends and test data to train and test different classifiers. The purpose of this thesis is to determine which Indian political party is doing the best for the general population by classifying Twitter data into feelings (positive or negative) using several supervised machines learning classifiers on data collected for different Indian political parties. We also concluded that some classifiers perform better than others.

"Twitter Sentiment Analysis in Python ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.m149-m153, April-2023, Available :http://www.jetir.org/papers/JETIR2304C16.pdf

"Twitter Sentiment Analysis in Python ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppm149-m153, April-2023, Available at : http://www.jetir.org/papers/JETIR2304C16.pdf