Sentiment Computing for the News Event Based on the Social Media Big Data

The explosive increasing of the social media data on the Web has created and promoted the development of the social media big data mining area welcomed by researchers from both academia and industry. The sentiment computing of news event is a significant component of the social media big data. It ha...

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Bibliographic Details
Published in:IEEE access 2017, Vol.5, p.2373-2382
Main Authors: Jiang, Dandan, Luo, Xiangfeng, Xuan, Junyu, Xu, Zheng
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Classification
Computation
Data mining
Digital media
emotion classification
Emotion recognition
Emotions
News
Sentiment computing
social media big data
Social network services
Social networks
Text mining
Websites
Words (language)
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Summary:The explosive increasing of the social media data on the Web has created and promoted the development of the social media big data mining area welcomed by researchers from both academia and industry. The sentiment computing of news event is a significant component of the social media big data. It has also attracted a lot of researches, which could support many real-world applications, such as public opinion monitoring for governments and news recommendation for Websites. However, existing sentiment computing methods are mainly based on the standard emotion thesaurus or supervised methods, which are not scalable to the social media big data. Therefore, we propose an innovative method to do the sentiment computing for news events. More specially, based on the social media data (i.e., words and emoticons) of a news event, a word emotion association network (WEAN) is built to jointly express its semantic and emotion, which lays the foundation for the news event sentiment computation. Based on WEAN, a word emotion computation algorithm is proposed to obtain the initial words emotion, which are further refined through the standard emotion thesaurus. With the words emotion in hand, we can compute every sentence's sentiment. Experimental results on real-world data sets demonstrate the excellent performance of the proposed method on the emotion computing for news events.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2016.2607218