关键词: 突发词探测, 滑动时间窗口, 多测度, LDA主题挖掘

Abstract: [Purpose/significance] In order to effectively detect potential research hotspots in scientific and technological literature, to study the characteristic conditions of keyword emergencies in the literature, and to construct a model of burst word recognition is of great significance to promote scientific researchers to accurately grasp the research direction. [Method/process] This paper got keywords and word frequency in each year, constructed keyword-year matrix, divided the analysis period into standard window, observation window and performance window, used multi-measure burst word detection model to identify keywords with burst characteristics in the observation window, and used LDA to mine topic words as hot words set in the performance window. The coverage index of burst words was designed, and the sliding time window method was used to calculate the coverage of burst words and hot words in different time windows to verify the accuracy of model recognition. [Result/conclusion] The three sliding time windows calculated that the coverage of the three sudden words is more than 70%. In the control test with Citespace, the coverage of the model three times is greater than the former, indicating that the designed burst word detection model performs well.

Key words: burst word detection, sliding time window, multiple measures, LDA topic mining