Exploring Sentiment Analysis: Applications, and Challenges —A Comprehensive Survey

Abstract

Sentiment analysis, also known as opinion mining, has emerged as a crucial field of research due to the exponential growth of user-generated content on various online platforms. This paper presents a comprehensive survey of sentiment analysis applications and challenges. It provides an overview of the different techniques employed for sentiment analysis, ranging from traditional machine learning approaches to more recent deep learning models. Furthermore, the survey examines the diverse applications of sentiment analysis across domains such as social media, e-commerce, customer reviews, and political analysis. Additionally, the paper highlights the major challenges and open research questions in sentiment analysis, including handling sarcasm, irony, and ambiguity, addressing data sparsity and imbalance issues, and ensuring cross-lingual and cross-domain generalization. By analyzing the existing literature, this survey aims to offer insights into the current state-of-the-art in sentiment analysis and provide directions for future research in this dynamic field. Key Words: Sentimental Analysis, languages, text, multimode, opinion