マルチタスク最大マージントピックモデルによる収益予測

抄録

Researchers and practitioners in the economic and financial field recently have a keen interest in discovering new ideas by making full use of large-scale data, such as in the form of document data of company valuation in online news and the form of numerical data of company financial indices. One promising approach to analyzing such large-scale data is topic modeling, typically by Maximum Entropy Discrimination LDA (MedLDA). MedLDA is a supervised topic model that can improve accuracy of latent topic estimation by making use of the side information associated with each document. In this paper, we generalize Multi-task MedLDA (MultiMedLDA) that simultaneously addresses classification and regression tasks in an extension of MedLDA. In this paper, we evaluate the effectiveness of MultiMedLDA through experiments with enterprise evaluation documents associated with continuous labels of change rate of operating incomes and discrete labels of categories of business, and discuss it compared with single-task MedLDA.