Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic modeling have been developed which consider many kinds of relationships and restrictions within datasets; however, these methods are not frequently employed. Instead many researchers gravitate to Latent Dirichlet Analysis, which although flexible and adaptive, is not always suited for modeling more complex data relationships. We present different topic modeling approaches capable of dealing with correlation between topics, the changes of topics over time, as well as the ability to handle short texts such as encountered in social media or sparse text data. We also briefly review the algorithms which are used to optimize and infer parameters in topic modeling, which is essential to producing meaningful results regardless of method. We believe this review will encourage more diversity when performing topic modeling and help determine what topic modeling method best suits the user needs.

主题建模是用于评估数据的流行分析工具。已经开发了许多主题建模方法，这些方法考虑了数据集中的多种关系和限制。但是，这些方法并不经常使用。取而代之的是，许多研究人员倾向于使用Latent Dirichlet分析，该方法虽然灵活且自适应，但并不总是适合于对更复杂的数据关系进行建模。我们提出了不同的主题建模方法，这些方法能够处理主题之间的相关性，主题随时间的变化以及处理短文本（例如在社交媒体中遇到的稀疏文本或稀疏文本数据）的能力。我们还将简要回顾用于优化和推断主题建模中参数的算法，这对于产生有意义的结果（无论使用哪种方法）都是必不可少的。