Each and every day there are lots and lots of contents being published on the web. May it be blogs, news articles, movie reviews, product reviews, etc. Although this many number of contents are published, how far a content reaches and engages a large number of expected audience is questionable and unpredictable. Our project aims to solve this issue. The goal of our project is to develop a system that, when a post or textual content is given as an input, makes it to go viral on the web. Our project is the first to propose such a solution for this problem. To make a post go viral, we follow two approaches. First one is to improve the content of the post to incorporate emotions and sentiments. The other one is to take a post directly to its potential audience. The initial step of the first approach is deriving some rules. The next step is where we analyze the actual post, measure its popularity and give suggestions as to how to improve the post in order to make it viral, based on the derived rules. The suggestions will be to replace certain words. The final part is the second approach towards solving the problem. Here, we analyze the social media posts of potential viewers of a post and understand what kind of decisions they may take in the future, so that we can recommend them directly with a certain post. Since we do the system for the domain of movies, we mine the text regarding a user's expectations to watch a certain kind of movie in the near future and recommend him with the reviews about the movie he may likely to watch.