Published Paper Details:

In recent years, social media platforms have emerged as critical sources of data, providing rich and diverse in formation about public opinions, behaviors, and emotions. Sentiment analysis and emotion detection in social media text have gained considerable attention, serving as essential tools for understanding user sentiments and emotional responses. This survey explores the advancements and methodologies employed in sentiment analysis and emotion detection, focusing on various techniques including rule based, machine learning, and deep learning approaches. It highlights the challenges in here not in analyzing informal and context-rich social media language, such as slang, emojis, and multi lingual content. Additionally, the survey discusses the role of pre-processing strategies, feature extraction methods, and the effectiveness of different models like LSTM, BERT, and Transformer based architectures in capturing complex emotional nuances. The paper also examines the applications of these techniques in diverse domains such as brand monitoring, crisis management, and mental health analysis. By providing a comprehensive overview of the existing literature and emerging trends, this survey aims to guide future research directions and improvements in sentiment analysis and emotion detection in the dynamic context of social media text.