  To handle emotional expressions in computer applications, Russell's circum-
plex model has been useful for representing emotions according to valence and
arousal. In SentiWordNet, the level of valence is automatically assigned to a
large number of synsets (groups of synonyms in WordNet) using semi-supervised
learning. However, when assigning the level of arousal, the existing method
proposed for SentiWordNet reduces the accuracy of sentiment prediction. In this
paper, we propose a block-segmentation vector for predicting the arousal levels
of many synsets from a small number of labeled words using semi-supervised
learning. We analyze the distribution of arousal and non-arousal words in a
corpus of sentences by comparing it with the distribution of valence words. We
address the problem that arousal level prediction fails when arousal and
non-arousal words are mixed together in some sentences. To capture the features
of such arousal and non-arousal words, we generate word vectors based on
inverted indexes by block IDs, where the corpus is divided into blocks in the
flow of sentences. In the evaluation experiment, we show that the results of
arousal prediction with the block-segmentation vectors outperform the results
of the previous method in SentiWordNet.
