Block-segmentation vectors for arousal prediction using semi-supervised learning

• Published in: Applied soft computing 2023-07, Vol.142, p.110327, Article 110327
• Main Authors: Odaka, Yuki, Kaneiwa, Ken
• Format: Article
• Language: English
• Subjects: Arousal; Semi-supervised learning; Sentiment analysis; Word embedding
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2023.110327

To handle emotional expressions in computer applications, Russell’s circumplex 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 using semi-supervised learning outperform the results of the previous methods in SentiWordNet and SocialSent. •Proposing a block-segmentation vector to predict arousal levels for WordNet synsets.•Analyzing word distribution shows labeling arousal is more difficult than valence.•Feature-selected block-segmentation vectors improve arousal prediction accuracy.•Segmenting a corpus into blocks detects arousal levels in long sentence contexts.