Paper Title
Comparative Sentiment Analysis of Twitter Data on Climate Conferences

Abstract
‘Sentiment Analysis’, one of the most powerful and widely used fields in Natural Language Processing (NLP), classifies the notions expressed in an image, text, etc., according to their polarity. Thus, sentiment analysis has applications in social media, an online website where users present their opinions and emotions about various happenings and events in the form of images, text, etc. One such event happened in Nov '22, Climate Change Conference (COP27), where a committee of the United Nations Framework Convention on Climate Change (UNFCCC) met regularly to discuss pertinent climate issues. With the advent of industrialization in the late 1900s, climate issues became quite the after effect. This is when COP came into existence. Although the UNFCCC committee was first established in 1992 (COP1) it was only in 2015 (COP21) that an ambitious agreement was signed by 196 members to fight global warming. Through this paper, we try to understand what the reaction (sentiment) of the public worldwide was to COP 21 through COP27 during the conference and a week after its conclusion. We do this by extracting and comparing tweets referring to COP using ‘VADER’. This work will enable society to extract useful insights and understand how people perceive this conference, especially the latest iteration of the climate conference. Index terms: Sentiment Analysis, COP27, Twitter, VADER, Climate Keywords - Sentiment Analysis, COP27, Twitter, VADER, Climate