Trend Keywords Extraction from Customer Web Behavior Log Data by using Naive Bayes and K-means for Internet Advertising

Abstract

Keyword extraction method from customer web behavior log data consists of anonymized user ID, timestamp data related with user ID, and URL where the user has accessed; is proposed to realize trend extraction with few contexts’ information in user activity logs by using naive bayes learned extra-dictionary for classification of document category and k-means to extract topic in each category. The proposal provides effective complement context information to customer web behavior log data, which is scattered in many topics, implements keyword extraction where a keyword includes word context in short text such as search queries in URL, and realizes trend information obtaining for internet advertise with low cost and short computation time. Typical word becomes mean vector for each cluster, which is a specific topic in given any period to analyze, is confirmed in experimental clustering trial applied to search queries in customer web behavior log data for seeking YouTube movies. The proposal provides more effective marketing direction via rapid observation of the trend and realizes variable and interactive digital communication for 5G era.