Published Paper Details:

Sentiment analysis, originating in the early 2000s, has evolved from rule-based methods to advanced machine learning models. Widely applied across industries, it deciphers opinions in textual data. Despite advancements, challenges persist, including handling sarcasm, context nuances, and cultural variations, prompting ongoing research to enhance its accuracy and versatility. This paper delves into the intricate realm of sentiment analysis, providing a comprehensive exploration of its fundamental principles and techniques. The study begins by elucidating the basics of sentiment analysis, unraveling the core concepts that form the foundation of this field. The paper scrutinizes how sentiment analysis has transcended the binary classification of positive and negative sentiments, venturing into nuanced analyses that capture the complexity of human emotions expressed in text. In addition to comprehensively covering the existing landscape, this paper engages in foresight, contemplating the future frontiers of sentiment analysis. We ponder the potential applications and advancements that may emerge as technology progresses, envisioning the role sentiment analysis might play in diverse industries.