Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN
Joint Authors
Xu, Zhiwei
Guo, Mengjie
Yun, Jing
Zhao, Siyuan
Liu, Limin
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-24
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Convolutional neural network (CNN) has revolutionized the field of natural language processing, which is considerably efficient at semantics analysis that underlies difficult natural language processing problems in a variety of domains.
The deceptive opinion detection is an important application of the existing CNN models.
The detection mechanism based on CNN models has better self-adaptability and can effectively identify all kinds of deceptive opinions.
Online opinions are quite short, varying in their types and content.
In order to effectively identify deceptive opinions, we need to comprehensively study the characteristics of deceptive opinions and explore novel characteristics besides the textual semantics and emotional polarity that have been widely used in text analysis.
In this paper, we optimize the convolutional neural network model by embedding the word order characteristics in its convolution layer and pooling layer, which makes convolutional neural network more suitable for short text classification and deceptive opinions detection.
The TensorFlow-based experiments demonstrate that the proposed detection mechanism achieves more accurate deceptive opinion detection results.
American Psychological Association (APA)
Zhao, Siyuan& Xu, Zhiwei& Liu, Limin& Guo, Mengjie& Yun, Jing. 2018. Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1206221
Modern Language Association (MLA)
Zhao, Siyuan…[et al.]. Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN. Mathematical Problems in Engineering No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1206221
American Medical Association (AMA)
Zhao, Siyuan& Xu, Zhiwei& Liu, Limin& Guo, Mengjie& Yun, Jing. Towards Accurate Deceptive Opinions Detection Based on Word Order-Preserving CNN. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1206221
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1206221