A Study on Affect Estimation Methods with Machine Learning from Training Data of Japanese Text

2015 Fiscal Year Final Research Report

• Project/Area Number: 24650061
• Research Category: Grant-in-Aid for Challenging Exploratory Research
• Allocation Type: Multi-year Fund
• Research Field: Intelligent informatics
• Research Institution: Hokkaido University
• Principal Investigator: Oyama Satoshi 北海道大学, 情報科学研究科, 准教授 (30346100)
• Project Period (FY): 2012-04-01 – 2016-03-31
• Keywords: 機械学習 / 感性情報学 / 人工知能 / クラウドソーシング

Outline of Final Research Achievements

Estimating affects from text is a key technology for many applications such as text-to-speech synthesis and affective education. In this study, we specifically dealt with the problem of estimating affects of characters’ utterances in narrative text. We enabled accurate affect estimation from crowdsourced labels by considering dependency between affect labels, affective tendency of the story and characters, and contextual cues in the story. We also developed algorithms for enabling interoperability between different affect models.

Free Research Field

人工知能