An Application of Recursive Neural Tensor Network to Sentiment Analysis of Japanese Sentences

Abstract

Recursive Neural Tensor Network (RNTN) is a neural network model that recursively computes the synthetic distributed vector representation for phrases of various lengths and syntax types from the distributed vector representation of words along the syntax tree. Distributed vector representation is used as a feature to classify each phrase and it is used to classify sentiment of phrases in sentiment analysis. In this paper, we apply the RNTN to sentiment analysis of Japanese sentences. For this purpose, based on the Stanford Sentiment Treebank corpus for sentiment analysis, we first create a corpus of Japanese sentences with teacher labels only for words and sentences. Then we evaluate the accuracy of sentiment analysis on Japanese sentences when we learn from only teacher labels for words and sentences. We also consider the effect of attaching teacher labels of phrases by heuristic rules.