Emotion Classification Using Web Blog Corpora

• Published in: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence pp. 275 - 278
• Main Authors: Yang, Changhua, Lin, Kevin Hsin-Yih, Chen, Hsin-Hsi
• Format: Conference Proceeding
• Language: English
• Published: Washington, DC, USA IEEE Computer Society 02.11.2007
• Series: ACM Conferences
• Subjects: Computing methodologies > Machine learning; Computing methodologies > Machine learning > Learning paradigms > Supervised learning > Supervised learning by classification; Computing methodologies > Machine learning > Machine learning approaches > Classification and regression trees; Computing methodologies > Modeling and simulation > Model development and analysis; Computing methodologies > Modeling and simulation > Model development and analysis > Model verification and validation; Human-centered computing > Collaborative and social computing; Human-centered computing > Human computer interaction (HCI)
• ISBN: 0769530265; 9780769530260
• DOI: 10.1109/WI.2007.50

In this paper, we investigate the emotion classification of web blog corpora using support vector machine (SVM) and conditional random field (CRF) machine learning techniques. The emotion classifiers are trained at the sentence level and applied to the document level. Our methods also determine an emotion category by taking the context of a sentence into account. Experiments show that CRF classifiers outperform SVM classifiers. When applying emotion classification to a blog at the document level, the emotion of the last sentence in a document plays an important role in determining the overall emotion.