  Comparative text mining extends from genre analysis and political bias
detection to the revelation of cultural and geographic differences, through to
the search for prior art across patents and scientific papers. These
applications use cross-collection topic modeling for the exploration,
clustering, and comparison of large sets of documents, such as digital
libraries. However, topic modeling on documents from different collections is
challenging because of domain-specific vocabulary. We present a
cross-collection topic model combined with automatic domain term extraction and
phrase segmentation. This model distinguishes collection-specific and
collection-independent words based on information entropy and reveals
commonalities and differences of multiple text collections. We evaluate our
model on patents, scientific papers, newspaper articles, forum posts, and
Wikipedia articles. In comparison to state-of-the-art cross-collection topic
modeling, our model achieves up to 13% higher topic coherence, up to 4% lower
perplexity, and up to 31% higher document classification accuracy. More
importantly, our approach is the first topic model that ensures disjunct
general and specific word distributions, resulting in clear-cut topic
representations.
