Emerging Trends in Sentiment Analysis within Natural Language Processing for Social Media

Abstract

Sentiment analysis, a vital domain of natural language processing (NLP), aims to classify textual data into sentiments such as positive, negative, or neutral. With the increased use of social media for expressing opinions, efficiently analyzing these sentiments has become crucial for organizations in sectors ranging from marketing and politics to finance and customer service. This paper reviews current methods and advancements in sentiment analysis, highlighting traditional and modern approaches, challenges with social media data, and proposing a robust, scalable framework using deep learning and transformer-based models. Comprehensive experimentation validates the effectiveness and outlines the future scope for more nuanced and multilingual sentiment evaluation.