Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67166
Title: Towards scalable emotion classification in microblog based on noisy training data
Authors: Li, M
Lu, Q 
Gui, L
Long, Y
Keywords: Data cleaning
Emotion classification
Hashtag
K-NN
Issue Date: 2016
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2016, v. 10035, p. 399-410 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: The availability of labeled corpus is of great importance for emotion classification tasks. Because manual labeling is too timeconsuming, 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.
Description: 15th China National Conference on Chinese Computational Linguistics, CCL 2016 and 4th International Symposium on Natural Language Processing Based on Naturally Annotated Big Data, NLP-NABD 2016, Yantai, China, 15-16 October 2016
URI: http://hdl.handle.net/10397/67166
ISBN: 9783319476735
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-47674-2_33
Appears in Collections:Conference Paper

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