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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Wei Song
Capital Normal University
Computer Science & IT

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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

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Research on Identification Method of Anonymous Fake Reviews in E-commerce Lizhen Liu; Xinlei Zhao; Hanshi Wang; Wei Song; Chao Du
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i4.3654

Abstract

In this paper, a new method has been proposed for identifying anonymous fake reviews generated by click farmers in E-commerce and improves the identification rates. Anonymous fake reviews are different from the gunuine reviews. They could be distinguished based on the credibility of users, the average daily number of evaluations, the content similarity, and the degree of word overlapping. The proposed method takes into account these 5 features to calculate the fake reviews content by constructing multivariate linear regression model, Experiments show that this prelimilnary work performed well in identifying fake reviews in Chinese E-commerce website. The extracted features are also useful to identifying the fake reviews when the reviewer’s identification is not accessable.
Automatic Summarization in Chinese Product Reviews Li zhen Liu; Wan di Du; Han shi Wang; Wei Song
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i1.5099

Abstract

With the increasing number of online comments, it was hard for buyers to find useful information in a short time so it made sense to do research on automatic summarization which fundamental work was focused on product reviews mining. Previous studies mainly focused on explicit features extraction whereas often ignored implicit features which hadn't been stated clearly but containing necessary information for analyzing comments. So how to quickly and accurately mine features from web reviews had important significance for summarization technology. In this paper, explicit features and “feature-opinion” pairs in the explicit sentences were extracted by Conditional Random Field and implicit product features were recognized by a bipartite graph model based on random walk algorithm. Then incorporating features and corresponding opinions into a structured text and the abstract was generated based on the extraction results. The experiment results demonstrated the proposed methods outpreferred baselines.
Co-Authors Chao Du Han shi Wang Hanshi Wang Li zhen Liu Lizhen Liu Wan di Du Xinlei Zhao