Identifying Concept-drift in Twitter Streams

We live in a Big Data society, where the dignity of data is like exchange of currency. What we produce as data affords as access to different application, benefits, services, delivery etc… In today's world communication is mainly through social networking sites like, Twitter, Facebook, and Google+. Huge amount of data that is being generated and shared across these micro-blogging sites, serves as a good source of Big Data Streams for analysis. As the topic of discussion changes drastically, the relevance of data is temporal, which leads to concept-drift. Identification and handling of this concept-drift in such Big Data Streams is present area of interest. The state-of-the-art techniques for identifying trending topics in such data streams mainly concentrates on the frequency of the topic as the key parameter. Concentrating on such a weak indicator, reduces the precision of mining. This study puts forward a novel approach towards identifying concept-drift by initially grouping topics into classes and assigning weight-age for each class, using sliding window processing model upon Twitter streams.