カンファレンス (国際) CSNN: Contextual Sentiment Neural Network

Tomoki Ito*, Kota Tsubouchi, Hiroki Sakaji*, Kiyoshi Izumi*, and Tatsuo Yamashita (* The University of Tokyo)

The IEEE International Conference on Data Mining (ICDM2019)


Although deep neural networks are excellent for text sentiment analysis, their applications in real-world practice are occasionally limited owing to their black-box property. In this study, we propose a novel neural network model called contextual sentiment neural network (CSNN) model that can explain the process of its sentiment analysis prediction in a way that humans find natural and agreeable. The CSNN has the following inter- pretable layers: the word-level original sentiment layer, word- level sentiment shift layer, word-level local contextual sentiment layer, word-level global importance layer, and word-level global contextual sentiment layer. Because of these layers, this network can explain the process of its document-level sentiment analysis results in a human-like way using these layers. Realizing the interpretablity of each layer in the CSNN is a crucial problem in the development of this CSNN because the general back- propagation method cannot realize such interpretability. To realize this interpretability, we propose a novel learning strategy called initialization propagation (IP) learning. Using real textual datasets, we experimentally demonstrate that the proposed IP learning is effective for realizing the high interpretability of each layer in CSNN. We then experimentally demonstrate that the developed CSNN has both the high predictability and high explanation ability.