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Topic modeling is a Natural Language Processing technique that has gained popularity over the last ten years, with applications in multiple fields of knowledge. However, there is insufficient empirical evidence to show how this field of study has developed over the years, as well as the main models that have been applied in different contexts. The objective of this paper is to analyze the evolution of the topic modeling technique, the main areas in which it has been applied, and the models that are recommended for specific types of data. The methodology applied is based on bibliometric analysis. First, we searched the Web of Science and the Scopus databases. We then used scientometric techniques and a Tree of Science methodology, which allowed us to analyze the search results from the perspectives of classics, structure, and trends. The results show that the USA and China are among the most productive countries in this field and the applications have been mainly in the identification of sub-topics in short texts, such as social networks and blogs. The main conclusion of this work is that topic modeling is a versatile technique that can complement systematic literature reviews and that has been well-received in different academic and research contexts. The results of this study will help researchers and academics to recognize the importance of these techniques for reviewing large volumes of unstructured information, such as research articles, and in general, for systematic literature reviews.
Aguirre et al. (Wed,) studied this question.