Hot topic detection in central bankers’ speeches

Park, Jihye; Lee, Hye Jin; Cho, Sungzoon

doi:10.1016/j.eswa.2023.120563

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…Qiao et al sorted out the existing subversive technology identification methods from the perspective of technology, the market, and a combination of technology and the market [13]. Park et al proposed a text mining framework to extract the main topics from speeches [14]. Savin et al identified around 20 topics related to service robotics by applying13 topic modeling [15].…”

Section: Research On Topic Identificationmentioning

confidence: 99%

Research Practice and Progress of Models and Algorithms Applied in Topic Identification and Prediction Based on the Analysis of CNKI

Guo

Liu

2023

Applied Sciences

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As a hot topic in the field of library and information, the research on topic recognition and trend prediction has been paid close attention by academic circles. This paper uses a systematic literature review, bibliometric analyses and classification methods. Through a systematic literature review, 96 studies about topic identification and evolution prediction models are selected from the CNKI database. By using VOSviewer to conduct bibliometric analyses, the key research content and themes are revealed. Through the classification method, EXCEL is used to summarize models and algorithms used in the literature comprehensively. It is found that topic identification models and algorithms can be divided into four categories: ① Topic model based on LDA and related derivative models. ② Machine learning and deep learning methods. ③ Methods based on reference relation. ④ Text mining methods. Trend prediction models and algorithms mainly cover two categories: ① deep learning or machine learning models and algorithms based on time sequence; ② link prediction algorithms based on complex network. At the same time, we have also summarized the common index system involved in each study and the way to evaluate the effectiveness of the method, thus this paper comprehensively reveals the application progress in academic circles of topic identification and prediction models and algorithms from the last 10 years and beyond, based on the CNKI database. The purpose is to determine the most popular models and algorithms applied in research, generalize the corresponding indicator systems and validation methods, and finally provide references for model choice or evaluation when identifying and predicting topics in the future. Thus, this paper can help us to understand the overall progress made in text analysis research, and provides a useful reference for selecting and applying the appropriate models, algorithms and indicators.

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Section: Research On Topic Identificationmentioning

confidence: 99%