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2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 215-222
Xin Li , Sun Yat-sen University, Guang Dong, China
Haoran Xie , Caritas Institute of Higher Education, Tseung Kwan O, New Territories, Hong Kong SAR, China
Yanghui Rao , Sun Yat-sen University, Guang Dong, China
Yanjia Chen , Sun Yat-sen University, Guang Dong, China
Xuebo Liu , Sun Yat-sen University, Guang Dong, China
Huan Huang , Sun Yat-sen University, Guang Dong, China
Fu Lee Wang , Caritas Institute of Higher Education, Tseung Kwan O, New Territories, Hong Kong SAR, China
ABSTRACT
With the extensive growth of social media services, many users express their feelings and opinions through news articles, blogs and tweets/microblogs. To discover the connections between emotions evoked in a user by varied-scale documents effectively, the paper is concerned with the problem of sentiment analysis over online news. Different from previous models which treat training documents uniformly, a weighted multi-label classification model (WMCM) is proposed by introducing the concept of "emotional concentration" to estimate the weight of training documents, in addition to tackle the issue of noisy samples for each emotion. The topic assignment is also used to distinguish different emotional senses of the same word at the semantic level. Experimental evaluations using short news headlines and long documents validate the effectiveness of the proposed WMCM for sentiment prediction.
INDEX TERMS
Training, Sentiment analysis, Algorithm design and analysis, Analytical models, Noise measurement, Classification algorithms, Prediction algorithms
CITATION

Xin Li et al., "Weighted multi-label classification model for sentiment analysis of online news," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 215-222.
doi:10.1109/BIGCOMP.2016.7425916
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