DocumentCode :
694672
Title :
Dividing for Combination: A Bootstrapping Sentiment Classification Framework for Micro-blogs
Author :
Songxian Xie ; Ting Wang
Author_Institution :
Dept. of Comput. Sci. & Technol., Nat. Univ. of Defense Technol., Changsha, China
fYear :
2013
fDate :
7-8 Dec. 2013
Firstpage :
78
Lastpage :
84
Abstract :
There are many challenges for sentiment classification of user-generated content (UGC) on social media platforms such as micro-blogs. Context dependence, which has been the most challenging problem, is focused on in this paper, and a novel semi-supervised framework is proposed to address the problem. By dividing the feature space of sentiment classification into two parts including the general features and the context features, a general classifier and a context classifier are learned separately in the two partial feature spaces, and a semi-supervised framework is developed to combine the general classifier and context classifier into a bootstrapping classifier. Experimental results show that both the general classifier and context classifier outperform traditional lexicon-based classifier, and the combined bootstrapping classifier outperforms supervised classifier upper bound. The proposed semi-supervised framework is flexible and effective in solving the context dependent problem of sentiment classification for micro-blogs without the need of labeled data.
Keywords :
learning (artificial intelligence); natural language processing; pattern classification; social networking (online); bootstrapping classifier; context classifier; context features; feature space; general classifier; general features; microblogs; semisupervised framework; sentiment classification; social media platforms; user-generated content; Accuracy; Context; Feature extraction; Sentiment analysis; Support vector machine classification; Training; classifier; context dependence; idioms; sentiment classification; social media;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Cloud Computing (ISCC), 2013 International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4799-4968-7
Type :
conf
DOI :
10.1109/ISCC.2013.18
Filename :
6972565
Link To Document :
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