Discovering Trending Phrases on Information Streams

摘要

We study the problem of efficient discovery of trending phrases from high-volume text streams be they sequences of Twitter messages, email messages, news articles, or other time-stamped text documents. Most exisiting approaches return top-k trending phrases. But. this approach neither guarantees that the top-k phrases returned are all trending, nor that all trending phrases are returned. In addition, the value of k is difficult to set and is indifferent to stream dynamics. Hence, we propose an approach that identifies all the trending phrases in a stream and is flexible to the changing stream properties.