Building a search engine to discovery web services specified using a natural language query that infers relationships using an ontology of Twitter data. Technologies used are NLTK, Python, Whoosh, Django and CMU Ark Tweet Parser. The fast information sharing on Twitter from millions of users all over the world leads to almost real-time reporting of events. It is extremely important for business and administrative decision makers to learn events popularity as quickly as possible, as it can buy extra precious time for them to make informed decisions. Therefore, we introduce the problem of predicting future popularity trend of events on microblogging platforms. Traditionally, trend prediction has been performed by using time series analysis of past popularity to forecast the future popularity changes.

A full featured microblogging web application written in Python-Flask that I developed with python-flask, html, css and javascript.