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Automatic Construction and Global Optimization of a Multisentiment Lexicon
Joint Authors
Yang, Xiaoping
Zhang, Zhongxia
Zhang, Zhongqiu
Mo, Yuting
Li, Lianbei
Yu, Li
Zhu, Peican
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-29
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Manual annotation of sentiment lexicons costs too much labor and time, and it is also difficult to get accurate quantification of emotional intensity.
Besides, the excessive emphasis on one specific field has greatly limited the applicability of domain sentiment lexicons (Wang et al., 2010).
This paper implements statistical training for large-scale Chinese corpus through neural network language model and proposes an automatic method of constructing a multidimensional sentiment lexicon based on constraints of coordinate offset.
In order to distinguish the sentiment polarities of those words which may express either positive or negative meanings in different contexts, we further present a sentiment disambiguation algorithm to increase the flexibility of our lexicon.
Lastly, we present a global optimization framework that provides a unified way to combine several human-annotated resources for learning our 10-dimensional sentiment lexicon SentiRuc.
Experiments show the superior performance of SentiRuc lexicon in category labeling test, intensity labeling test, and sentiment classification tasks.
It is worth mentioning that, in intensity label test, SentiRuc outperforms the second place by 21 percent.
American Psychological Association (APA)
Yang, Xiaoping& Zhang, Zhongxia& Zhang, Zhongqiu& Mo, Yuting& Li, Lianbei& Yu, Li…[et al.]. 2016. Automatic Construction and Global Optimization of a Multisentiment Lexicon. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1099593
Modern Language Association (MLA)
Yang, Xiaoping…[et al.]. Automatic Construction and Global Optimization of a Multisentiment Lexicon. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-8.
https://search.emarefa.net/detail/BIM-1099593
American Medical Association (AMA)
Yang, Xiaoping& Zhang, Zhongxia& Zhang, Zhongqiu& Mo, Yuting& Li, Lianbei& Yu, Li…[et al.]. Automatic Construction and Global Optimization of a Multisentiment Lexicon. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1099593
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099593