Towards Scalable Emotion Classification in Microblog Based on Noisy Training Data

Abstract

The availability of labeled corpus is of great importance for emotion classification tasks. Because manual labeling is too time-consuming, hashtags have been used as naturally annotated labels to obtain large amount of labeled training data from microblog. However, the inconsistency and noise in annotation can adversely affect the data quality and thus the performance when used to train a classifier. In this paper, we propose a classification framework which allows naturally annotated data to be used as additional training data and employs a k-NN graph based data cleaning method to remove noise after noisy data has certain accumulations. Evaluation on NLP&CC2013 Chinese Weibo emotion classification dataset shows that our approach achieves 15.8 % better performance than directly using the noisy data without noise filtering. After adding the filtered data with hashtags into an existing high-quality training data, the performance increases 3.7 % compared to using the high-quality training data alone.