An Accurate Topic Mining Algorithm Based on Business Dictionary

Abstract

The text mining is an important branch of data mining. Many scientific research institutions and teams are actively exploring and putting forward algorithms. Because of industry and scene difference, it is difficult to use the common analysis algorithm of log to mine the potential information accurately. For example, a topic is given in one scene, how to find the main related words is not easy. To deal with the problem, this paper provides the accurate topic mining algorithm based on business dictionary. In the algorithm, segmenting with business dictionary is achieved in the document set after screening the valid documents. In this step, the document set is split into professional terms and then the invalid words are removed. Finally, the qualitative analysis is transformed to quantitative analysis. With the relevance index, the relevance degree of every word is computed. The relevance matrix is returned to the user to analyze the relevance of the words and topic. The algorithm has been applied to PMS and the validation result shows the main related factors can be analyzed accurately.