  Micro-blogging service Twitter is a lucrative source for data mining
applications on global sentiment. But due to the omnifariousness of the
subjects mentioned in each data item; it is inefficient to run a data mining
algorithm on the raw data. This paper discusses an algorithm to accurately
classify the entire stream in to a given number of mutually exclusive
collectively exhaustive streams upon each of which the data mining algorithm
can be run separately yielding more relevant results with a high efficiency.
