Improving tweet stream classification by detecting changes in word probability
We propose a classification model of tweet streams in Twitter, which are representative of document streams whose statistical properties will change over time. Our model solves several problems that hinder the classification of tweets; in particular, the problem that the probabilities of word occurrence change at different rates for different words. Our model switches between two probability estimates based on full and recent data for each word when detecting changes in word probability. This switching enables our model to achieve both accurate learning of stationary words and quick response to bursty words. We then explain how to implement our model by using a word suffix array, which is a full-text search index. Using the word suffix array allows our model to handle the temporal attributes of word n-grams effectively. Experiments on three tweet data sets demonstrate that our model offers statistically significant higher topic-classification accuracy than conventional temporally-aware classification models.