Leveraging AI for Real-Time Sentiment Analysis in Social Media Networks
Abstract
The rise of social media platforms has led to an unprecedented volume of user-generated content, creating an opportunity for real-time sentiment analysis to understand public opinion and behavior. This paper explores the application of artificial intelligence (AI) in real-time sentiment analysis of social media networks, focusing on the integration of natural language processing (NLP) and machine learning (ML) techniques to analyze posts, tweets, comments, and other forms of social media content. By leveraging AI algorithms, sentiment analysis models can classify content as positive, negative, or neutral, providing valuable insights into consumer behavior, political opinions, and public sentiment. The paper reviews the various AI techniques used for sentiment analysis, discusses the challenges of processing unstructured data from social media, and presents a case study demonstrating the effectiveness of AI in real-time sentiment detection. The study highlights the potential of AI-driven sentiment analysis in applications ranging from marketing and customer service to crisis management and social research.
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