Hierarchical emotion classification and emotion component analysis on chinese micro-blog posts

Text emotion analysis has long been a hot topic. With the development of social network, text emotion analysis on micro-blog posts becomes a new trend in recent years. However, most researchers classify posts into coarse-grained emotion classes, which cannot depict the emotions accurately. Besides, flat classification is mostly adopted, which brings difficulty for classifiers when given a large dataset. In this paper, by data preprocessing, feature extraction and feature selection, we classify Chinese micro-blog posts into fine-grained emotion classes, employing hierarchical classification to improve the performance of classifiers. Moreover, based on the regression values in classification procedure, we propose an algorithm to detect the principal emotions in posts and calculate their ratios.