  We propose an Exclusive Topic Modeling (ETM) for unsupervised text
classification, which is able to 1) identify the field-specific keywords though
less frequently appeared and 2) deliver well-structured topics with exclusive
words. In particular, a weighted Lasso penalty is imposed to reduce the
dominance of the frequently appearing yet less relevant words automatically,
and a pairwise Kullback-Leibler divergence penalty is used to implement topics
separation. Simulation studies demonstrate that the ETM detects the
field-specific keywords, while LDA fails. When applying to the benchmark NIPS
dataset, the topic coherence score on average improves by 22% and 10% for the
model with weighted Lasso penalty and pairwise Kullback-Leibler divergence
penalty, respectively.
