Applications of Natural Language Processing in Social Media Sentiment Analysis

Abstract

Natural Language Processing (NLP) has emerged as a powerful tool for analyzing social media data, enabling insights into public sentiment, trends, and behavioral patterns. Social media platforms generate vast amounts of unstructured text data, which presents both opportunities and challenges for researchers and businesses. This paper explores the applications of NLP in social media sentiment analysis, highlighting its ability to process, analyze, and interpret user-generated content. Key methodologies, including sentiment classification, topic modeling, and aspect-based sentiment analysis, are discussed alongside recent advancements in machine learning models such as transformer-based architectures (e.g., BERT, GPT). The study examines the effectiveness of NLP techniques in identifying sentiment trends across various industries, such as marketing, politics, and healthcare, and addresses challenges like handling sarcasm, context sensitivity, and multilingual data. By showcasing real-world applications and future directions, this paper underscores the critical role of NLP in unlocking actionable insights from social media sentiment analysis, driving data-informed decision-making in the digital age.