  Text analysis is an interesting research area in data science and has various
applications, such as in artificial intelligence, biomedical research, and
engineering. We review popular methods for text analysis, ranging from topic
modeling to the recent neural language models. In particular, we review
Topic-SCORE, a statistical approach to topic modeling, and discuss how to use
it to analyze MADStat - a dataset on statistical publications that we collected
and cleaned.
  The application of Topic-SCORE and other methods on MADStat leads to
interesting findings. For example, $11$ representative topics in statistics are
identified. For each journal, the evolution of topic weights over time can be
visualized, and these results are used to analyze the trends in statistical
research. In particular, we propose a new statistical model for ranking the
citation impacts of $11$ topics, and we also build a cross-topic citation graph
to illustrate how research results on different topics spread to one another.
  The results on MADStat provide a data-driven picture of the statistical
research in $1975$--$2015$, from a text analysis perspective.
