Block-Segmentation Vectors for Arousal Prediction using Semi-supervised Learning

TL;DR

This paper introduces a novel block-segmentation vector method for semi-supervised arousal prediction in sentiment analysis, improving accuracy over previous approaches by better capturing arousal-related features in text.

Contribution

The paper proposes a new block-segmentation vector technique for semi-supervised arousal prediction, addressing limitations of existing methods in mixed arousal and non-arousal contexts.

Findings

Block-segmentation vectors outperform previous methods in arousal prediction.

The approach effectively captures arousal features in sentence-based corpora.

Improved accuracy demonstrated in SentiWordNet evaluations.

Abstract

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.