  Efficient textual data distributions (TDD) alignment and generation are open
research problems in textual analytics and NLP. It is presently difficult to
parsimoniously and methodologically confirm that two or more natural language
datasets belong to similar distributions, and to identify the extent to which
textual data possess alignment. This study focuses on addressing a segment of
the broader problem described above by applying multiple supervised and
unsupervised machine learning (ML) methods to explore the behavior of TDD by
(i) topical alignment, and (ii) by sentiment alignment. Furthermore we use
multiple text generation methods including fine-tuned GPT-2, to generate text
by topic and by sentiment. Finally we develop a unique process driven variation
of Kullback-Leibler divergence (KLD) application to TDD, named KL Textual
Distributions Contrasts(KL-TDC) to identify the alignment of machine generated
textual corpora with naturally occurring textual corpora. This study thus
identifies a unique approach for generating and validating TDD by topic and
sentiment, which can be used to help address sparse data problems and other
research, practice and classroom situations in need of artificially generated
topic or sentiment aligned textual data.

Computation and Language;Machine Learning;Social and Information Networks