The rapid growth of internet-based applications, such as social media platforms and blogs, has led to an increase in comments and reviews about everyday activities. Sentiment analysis involves collecting and analysing people’s opinions, thoughts, and perceptions on various topics, products, services, and subjects. These opinions can provide valuable insights for businesses, governments, and individuals in making informed decisions. However, the process of sentiment analysis faces several challenges that make it difficult to accurately interpret sentiments and determine the correct sentiment polarity. Sentiment analysis extracts subjective information from text using natural language processing (NLP) and text mining techniques. This article provides an in-depth overview of the methods used to perform sentiment analysis, along with its applications. It also evaluates and compares different approaches, discussing their advantages and limitations. Finally, the article examines the challenges in sentiment analysis and proposes future directions for the field. Sentiment analysis, also referred to as opinion mining, is a vital area of research in natural language processing (NLP) that focuses on identifying the sentiment expressed in text. This paper reviews various sentiment analysis techniques, explores its broad range of applications, and discusses the challenges within the field. The goal is to provide a thorough understanding of the current state of sentiment analysis and its potential future developments.