Nowadays, chatbots has emerged in all domains and proved its efficiency in helping assistants saving time and managing interactions with customers. However, communicating with a conversational agent seems frustrating sometimes, especially when the goal of this chatbot is to help users to overcome their problems. That is why, NLP researchers has developed a new terminology that aims to make conversations with virtual assistants hat does not sound or behave like robots but as human like as possible. The tool which can enhance this is sentiment analysis. In this context, Pixemantic decides to include a Positive chatbot in its new platform called Dr.Happy, that aims to help users overcome their depression, anxiety, and daily-life problems.In order to achieve its goal, Pixemantic should have a solid dataset that allows to the chatbot to discuss in different topics with users and assist them with solutions for their issues. we will cover the main milestones of the preparation of the needed dataset for Positive chatbot