Sentiment analysis is contextual mining of text which identifies and extracts subjective information in the source material and helps a business to understand the social sentiment of their brand, product, or service while monitoring online conversations. Analysis of social media streams is usually restricted to just basic sentiment analysis and count-based metrics. Automatic systems that rely on machine learning techniques to learn from data Hybrid systems. Uses of sentiment analysis include Surveys, text analytics, Forecast sales, and much more. Sentiment analysis is an automated process capable of understanding the feelings or opinions that underlie a text. One fundamental problem in sentiment analysis is the categorization of sentiment polarity. Compared to previous approaches in sentiment topics, additional findings showed that adding the semantic feature produces better Recall to compute the score) in negative sentiment classification. Approaches of Sentiment Analysis depend on how you wish to interpret client feedback and inquiries, you can define and customize your categories to match your sentiment analysis needs. The advantages of sentiment analysis are discussed in the paper. Sentiment analysis will enable you to have all kinds of market research and competitive analysis. While some NLP models are more emotionally intelligent than others, sentiment classification systems generally use one of the three algorithms: Rule-Based Systems, Automated Systems, or Hybrid Systems. We can clearly see that sentiment analysis is becoming more popular as e-commerce, SaaS solutions, and digital technologies advance.