[2L1-GS-2] Machine learning: NLP

[2L1-GS-2-01] An Analytical Model of Changes in Problems over Stages of Child-rearing from Question Data on a Dedicated Application

〇Koki Yamada¹, Ayako Yamagiwa¹, Yosuke Takao¹, Masayuki Goto¹ (1. Waseda University)

Keywords:Question data, Topic model, Mixture of unigram models, Topic transition, Sankey diagram

Dedicated Apps for child-caring QA system are widely used by many parents, on which users can ask questions about child-rearing. Problems that parents have should be changed depending on their children's life stage. If we can comprehend the problems that parents who grow small children have from the question data, it can contribute to the improvement of the users' satisfaction for a QA service by giving users proper information with correct timing based on problems they have. In this study, we propose an analytical model which can extract the topics of users' problems from question data and acquire their topic transitions over the stages of child-rearing. Furthermore, by taking a probabilistic view of the topic transitions, we construct a method for estimating the topic that is most likely to shift from a previous topic at a given time to the next non-question period. Finally, we show the results of applying the proposed method to the real data and conduct an evaluation experiment to show the usefulness of the estimation result by the proposed method.