Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN

by   Siyuan Zhao, et al.
Institute of Computing Technology, Chinese Academy of Sciences

As a mainly network of Internet naval activities, the deceptive opinion spam is of great harm. The identification of deceptive opinion spam is of great importance because of the rapid and dramatic development of Internet. The effective distinguish between positive and deceptive opinion plays an important role in maintaining and improving the Internet environment. Deceptive opinion spam is very short, varied type and content. In order to effectively identify deceptive opinion, expect for the textual semantics and emotional polarity that have been widely used in text analysis, we need to further summarize the deep features of deceptive opinion in order to characterize deceptive opinion effectively. In this paper, we use the traditional convolution neural network and improve it from the point of the word order by using the method called word order-preserving k-max pooling, which makes convolution neural network more suitable for text classification. The experiment can get better deceptive opinion spam detection.


page 1

page 2

page 3

page 4

page 5

page 6

page 7


Comparative Opinion Mining: A Review

Opinion mining refers to the use of natural language processing, text an...

Rating the Crisis of Online Public Opinion Using a Multi-Level Index System

Online public opinion usually spreads rapidly and widely, thus a small i...

User-based Network Embedding for Collective Opinion Spammer Detection

Due to the huge commercial interests behind online reviews, a tremendous...

Interpretable Text Classification Using CNN and Max-pooling

Deep neural networks have been widely used in text classification. Howev...

Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

Convolutional neural network (CNN) is a neural network that can make use...

Neural Transition System for End-to-End Opinion Role Labeling

Unified opinion role labeling (ORL) aims to detect all possible opinion ...

Electoral Forecasting Using a Novel Temporal Attenuation Model: Predicting the US Presidential Elections

Electoral forecasting is an ongoing scientific challenge with high socia...

Please sign up or login with your details

Forgot password? Click here to reset