On regime changes in text data using hidden Markov model of contaminated vMF distribution

This paper presents a novel methodology for analyzing temporal directional data with scatter and heavy tails. A hidden Markov model with contaminated von Mises-Fisher emission distribution is developed. The model is implemented using forward and backward selection approach that provides additional flexibility for contaminated as well as non-contaminated data. The utility of the method for finding homogeneous time blocks (regimes) is demonstrated on several experimental settings and two real-life text data sets containing presidential addresses and corporate financial statements respectively.