Subjects: Library Science,Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract: [Purpose/significance] The identification results of the LDA model is sometimes unsatisfactory due to some meaningless topics mixed together. Therefore, it's quite necessary to establish an effective topic filtering method to eliminate these noise topics and to ensure the accuracy of subsequent evolution analysis.[Method/process] Based on the co-occurrence relationship between keywords, keywords relevance index (KRI) was constructed. Taking the field of single cell research as an example, KRI values of the distribution of theme-keywords were calculated and compared with the results of manual interpretation.[Result/conclusion] Experimental results show that this method can effectively eliminate meaningless noise topics in the LDA model recognition results, which can improve the accuracy of topic recognition and the subsequent topic evolution analysis. It also helps to reduce the dependence on manual interpretation in the process of topic identification through the topic model method.