Affiliations: [a]
School of Computer Science, North China Institute of Science and Technology, Beijing, China
| [b]
Nanjing University High-Tech Institute at Suzhou, Suzhou, China
| [c] State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China
| [d] State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China
| [e]
Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China
| [f] College of Economics and Management, Beijing University of Technology, Beijing, China
Correspondence:
[*]
Corresponding authors. Lei Shi, State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China. E-mail: [email protected] and Tianbin Li, State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China. E-mail: [email protected].
Abstract: With the rapid development of information science and social networks, the Internet has accumulated various data containing valuable information and topics. The topic model has become one of the primary semantic modeling and classification methods. It has been widely studied in academia and industry. However, most topic models only focus on long texts and often suffer from semantic sparsity problems. The sparse, short text content and irregular data have brought major challenges to the application of topic models in semantic modeling and topic discovery. To overcome these challenges, researchers have explored topic models and achieved excellent results. However, most of the current topic models are applicable to a specific model task. The majority of current reviews ignore the whole-cycle perspective and framework. It brings great challenges for novices to learn topic models. To deal with the above challenges, we investigate more than a hundred papers on topic models and summarize the research progress on the entire topic model process, including theory, method, datasets, and evaluation indicator. In addition, we also analyzed the statistical data results of the topic model through experiments and introduced its applications in different fields. The paper provides a whole-cycle learning path for novices. It encourages researchers to give more attention to the topic model algorithm and the theory itself without paying extra attention to understanding the relevant datasets, evaluation methods and latest progress.
Keywords: Topic model, text mining, semantic understanding, whole-cycle, topic detection
