An Iterative Emotion Classification Approach for Microblogs

Ruifeng Xu, Zhaoyu Wang, Jun Xu, Junwen Chen, Qin Lu, Kam-Fai Wong

Abstract: The typical emotion classification approach adopts one-step single-label classification using intra-sentence features such as unigrams, bigrams and emotion words. However, single-label classifier with intra-sentence features cannot ensure good performance for short microblogs text which has flexible expressions. Target to this problem, this paper proposes an iterative multi-label emotion classification approach for microblogs by incorporating intra-sentence features, as well as sentence and document contextual information. Based on the prediction of the base classifier with intra-sentence features, the iterative approach updates the prediction by further incorporating both sentence and document contextual information until the classification results converge. Experimental results obtained by three different multi-label classifiers on NLP & CC2013 Chinese microblog emotion classification bakeoff dataset demonstrates the effectiveness of our iterative emotion classification approach.