Natural language processing (NLP) has become a main context for many websites. When we consider the case of social networking websites, many posts and conversations in the present day lacks in punctuation and vocabulary. People use short forms such as wru (where are you), hru (how are you) etc., in messages and comments in social networking sites. The comments with respect to the posts need to be categorized into positive, neutral or negative comments. Suppose a person post a status and people start commenting on it, there should be a measure above the post indicating the percentage of positive, negative and neutral comments. Hence, there is need for developing a tool-tip translator, which expands such short forms when mouse cursor is placed on them. There is also a need for developing a text classification system that classifies text based on polarity. Positive comments will be marked in green colour and negative comments will be marked in red colour and neutral in yellow. This paper shows how to implement such a tooltip translator and text classification system.