EMBAR

Author:
Dr. Manrab Sheikh
Professor, UNIVD University , Singapore

Published Date: 24-Aug, 2024

Keywords: Sentiment Analysis, Natural Language Processing, Social Media, Machine Learning, Public Opinion.

Abstract:
Sentiment analysis has emerged as a pivotal tool in understanding public opinion and behavior through the analysis of textual data from social media platforms. This research paper explores the application of Natural Language Processing (NLP) techniques in sentiment analysis, focusing on its effectiveness in brand monitoring, political analysis, public health monitoring, and market research. By leveraging advanced machine learning and deep learning models, such as Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and transformer-based models like BERT, sentiment analysis enables the accurate classification of sentiments expressed in social media content. This paper also addresses the unique challenges posed by social media data, including the detection of sarcasm, irony, and context-dependent sentiments, as well as the ethical considerations in data collection and privacy. Furthermore, the study examines the future outlook of sentiment analysis, highlighting potential advancements in NLP technologies that could further enhance its applications across various domains. The findings suggest that while significant progress has been made, ongoing research and innovation are essential to overcoming current limitations and maximizing the potential of sentiment analysis in social media.

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