• P Saad Khan
• Mr.N.Koteswara Rao

Social media serves as a fascinating platform for directly gauging people's emotions. Communication technologies are crucial in pinpointing these emotions across different geographical locations. However, understanding these emotions is no easy feat. The abundance of data generated on social media complicates the manual process. Additionally, the diversity of languages used in social media poses a challenge for automated approaches. Social networks facilitate discussions on various controversial topics, which in turn contribute to the field of data analysis. In this study, we aim to analyze tweets related to the political analysis. We collect tweets from users and employ a machine learning algorithm to classify the polarity of these tweets. Twitter is a well-known micro-blogging platform that is widely used to monitor public sentiment towards a particular object or entity. It involves the application of sentiment analysis on natural language to extract the emotions expressed by users. Analyzing sentiment on Twitter can be challenging due to the unprocessed nature of the text, limited character count, use of slang words, and abbreviations, which complicates the task of processing tweets. However, the importance of Twitter data cannot be overlooked as it reflects sentiments on a wide range of topics. This research focuses on analyzing the political orientation of Twitter users

Sentiment Analysis, Feature Extraction, Opinion Mining, Machine learning algorithm , Support Vector Machine, Social Network

"QUANTITATIVE POLITICAL SENTIMENT ANALYSIS USING NLP AND TWEETS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.204-208, June-2024, Available :http://www.jetir.org/papers/JETIRGL06035.pdf

"QUANTITATIVE POLITICAL SENTIMENT ANALYSIS USING NLP AND TWEETS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp204-208, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06035.pdf