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Abstract
The significance of real-time sentiment analysis has grown, particularly in the context of social media platforms. This research paper introduces a real-time sentiment analytics system powered by Natural Language Processing (NLP) techniques aimed at monitoring social media conversations and providing insights into public sentiment. With the proliferation of social media platforms, sentiment analysis helps businesses, researchers, and policymakers gauge public opinion and make informed decisions. The paper outlines the methodologies involved in extracting, processing, and analyzing social media data, followed by a discussion of key challenges such as data noise, ambiguity in sentiment expression, and scalability. A system architecture is proposed, incorporating NLP tools like text preprocessing, sentiment classification, and real-time data streaming, with the use of machine learning models to improve the accuracy of sentiment predictions. This research also highlights the practical applications of sentiment analysis in market research, brand monitoring, and political campaigning.
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