Social Media Sentiment Analysis Employing Deep Learning and Natural Language Processing (NLP)

Abstract

Sentiment analysis has grown in importance in recent years as a means of comprehending public opinion in a variety of contexts, including the internet, consumer feedback, and political debate. This essay explores the advancements in sentiment analysis in social media, particularly focusing on the use of natural language processing (NLPs) techniques. It examines how effectively deep learning and advanced machine learning models manage language and cultural diversity. Special attention is given to challenges such as code-switching, cross- lingual transfer, and the development of multilingual emotion lexicons. The research also investigates the effects of real-time sentiment analysis tools and contextual elements like slang and sarcasm. Our findings emphasize the significance of context- aware systems and the necessity for further research to overcome the limitations of existing methods. This thorough overview aims to offer valuable insights for potential future research in the area of multilingual sentiment analysis.