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.

翻译：文本分析是数据科学中一个有趣的研究领域，在人工智能、生物医学研究和工程等领域具有多种应用。本文回顾了从主题建模到近期神经语言模型的流行文本分析方法。特别地，我们介绍了主题建模的统计方法Topic-SCORE，并讨论了如何利用它分析MADStat——一个我们收集并清理的统计出版物数据集。将Topic-SCORE及其他方法应用于MADStat得出了有趣的发现。例如，识别出统计学中$11$个代表性主题。对于每种期刊，可以可视化主题权重随时间的变化，这些结果被用于分析统计研究趋势。此外，我们提出了一种新的统计模型来对$11$个主题的引文影响力进行排序，并构建了一个跨主题引文图，以说明不同主题的研究成果如何相互传播。基于MADStat的结果，从文本分析视角呈现了$1975$年至$2015$年间统计研究的数据驱动图景。