In natural language understanding, there is a hierarchy of lenses through which we can extract meaning - from words to sentences to paragraphs to documents. At the document level, one of the most useful ways to understand text is by analyzing its topics.
tomotopy is a Python extension of tomoto (Topic Modeling Tool) which is a Gibbs-sampling based topic model library written in C++. It utilizes a vectorization of modern CPUs for maximizing speed. The current version of tomoto supports several major topic models including
Y. Zuo, J. Wu, H. Zhang, H. Lin, F. Wang, K. Xu, и H. Xiong. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, стр. 2105–2114. New York, NY, USA, Association for Computing Machinery, (2016)