Sentiment Classification Using Negative and Intensive Sentiment Supplement Information

Sentiment Classification Using Negative and Intensive Sentiment Supplement Information

Abstract Traditional methods of annotating the sentiment of an unlabeled document are based on sentiment lexicons or machine learning algorithms, which have shown low computational cost or competitive performance. However, these methods ignore the semantic composition problem displaying in several w...

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Bibliographic Details
Main Authors: Xingming Chen, Yanghui Rao, Haoran Xie, Fu Lee Wang, Yingchao Zhao, Jian Yin
Format: Article
Language:English
Published: SpringerOpen 2019-06-01
Series:Data Science and Engineering
Subjects:
Negative words
Intensive words
Sentiment supplementary information
Online Access:http://link.springer.com/article/10.1007/s41019-019-0094-8

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http://link.springer.com/article/10.1007/s41019-019-0094-8

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