The article provides a comprehensive overview of sentiment analysis, an artificial intelligence subfield focused on interpreting emotions in text. It highlights the significance of sentiment analysis in evaluating customer feedback and its applications in business decision-making. The text outlines three primary approaches: rule-based, automatic, and hybrid, each with its methods for deciphering text sentiment. The article also discusses various types of sentiment analysis, such as graded, emotion detection, aspect-based, and intent-based analysis, which help extract nuanced insights from text data. While sentiment analysis offers advantages like real-time insights, scalability, and objectivity, it also faces challenges, including contextual understanding, data quality, and the inherent subjectivity of language. The conclusion emphasizes the transformative potential of sentiment analysis for businesses, while acknowledging the challenges in building effective models and sourcing high-quality datasets.