Online, you can find numerous recipes, some of which may be accurate while others may not. In this study, we present a data mining approach for identifying comments related to food recipes. We will employ a classification method to extract valuable insights from the data. By evaluating user reviews, the system will assign ratings to the recipes, making it easier for users to find reliable ones. This system proves beneficial for individuals seeking recipes online. Our plan involves training datasets comprising various food dishes, aiming to achieve precise and efficient detection of comments. This endeavor will contribute to the development of a robust application for food enthusiasts, addressing their concerns. In future it happens the real time system which is very helpful for foodies. Sentiment analysis in web embraces the problem of aggregating data in the web and extraction about opinions. Studying the opinions of customers helps to determine the people feeling about a product and how it is received in the market. Various commercial tools are available for sentiment analysis. In future in this system we will also proposed add new feature which users don’t know.