Habitual behavior extraction with statistical topic model from the internet

Abstract

Many research studies are being conducted about the analysis of human behavior using sensor devices in the real world, and a variety of information can be found all over Internet. The primary objective is to improve social behavior and habits, such as the prohibition against smoking and the use mobile phones while driving. These unhealthy social behaviors and habits tend to cause health problems and antisocial behaviors. Behavioral modification specialists understand that habitual behavior is one of the most important behaviors in solving these issues. This chapter proposes a new method to extract habitual behaviors for discovering the objectives of the behavioral modification. Specifically, Latent Dirichlet Allocation (LDA), is used for clustering words into appropriate topics of periodical behaviors from literary expressions, and Point-wise Mutual Information (PMI), is applied to select suitable words for habitual behaviors. The technique by using text data from question-answering websites from the telecommunications industry and transportation area was evaluated and showed the performance result.