カウンセリングデータにおけるトピックモデルを用いた文書分類

抄録

In order to build a highly efficient psychological counseling system, we provide a text classification method to aid the system in processing the complicated psychological knowledge base. This paper presents a method of text classification, which include LDA, morpheme parse, and majority voting. We believe that by classifying the user's question, it will increase the precision of finding the relevant answer to the user's question. We firstly collected three categories of psychological problem texts which are love-related, interpersonal relationship, self-knowledge which are questioned most commonly as training data and texting data. And all of the texts have tags that show their categories. Then we use these data to train LDA to obtain the most usable topic distributions of each category and most usable word distributions of each topic. We use a majority voting method to classify category unknown input with using these topic distributions and word distributions. We also did comparison experiments which were based on TF·IDF and SVM so as to identify the validity of our approach. The experimental results demonstrated the feasibility and effectiveness of our approach. According to the classification experiment, precision rate of our approach exceeds 80.3%, while the two comparison methods got 62.6% and 68.0%. We think by using our approach, counseling system can provide more accurate and effective answer to user.