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Learning Sparse Overcomplete Word Vectors Without Intermediate Dense Representations Read chapter Read first chapter

2017 | OriginalPaper | Chapter

A Hidden Astroturfing Detection Approach Base on Emotion Analysis

Authors : Tong Chen, Noora Hashim Alallaq, Wenjia Niu, Yingdi Wang, Xiaoxuan Bai, Jingjing Liu, Yingxiao Xiang, Tong Wu, Jiqiang Liu

Published in: Knowledge Science, Engineering and Management

Publisher: Springer International Publishing

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Abstract

This paper aims to take detection of hidden astroturfing based on emotion analysis. We propose a hidden astroturfing detection method which combines emotion analysis and unfair rating detection together. This approach contains five functional modules as: a data crawling module, pre-processing module, bag-of-word establishment module, emotion mining and analysis module and matching module. We give ROC curve (AUC) to evaluate the approach proposed in this paper. The results show that this method can realize the detection of implicit astroturfing under the prerequisite of improving the emotion classification accuracy. Our work discovers and studies a new hidden astroturfing characteristic, and construct a corpus manually for text emotion classification that establish a basis for our future research.

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previous chapter An Effective Gated and Attention-Based Neural Network Model for Fine-Grained Financial Target-Dependent Sentiment Analysis
next chapter Leveraging Term Co-occurrence Distance and Strong Classification Features for Short Text Feature Selection
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Metadata
Title
A Hidden Astroturfing Detection Approach Base on Emotion Analysis
Authors
Tong Chen
Noora Hashim Alallaq
Wenjia Niu
Yingdi Wang
Xiaoxuan Bai
Jingjing Liu
Yingxiao Xiang
Tong Wu
Jiqiang Liu
Copyright Year
2017
Book
Knowledge Science, Engineering and Management

Print ISBN: 978-3-319-63557-6

Electronic ISBN: 978-3-319-63558-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63558-3_5

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