Fake Review Detection with Concept Drift in the Data: A Survey

被引：3

作者：

Desale, Ketan Sanjay ^{[1
]}

Shinde, Swati ^{[1
]}

Magar, Nikita ^{[1
]}

Kullolli, Snehal ^{[1
]}

Kurhade, Anjali ^{[1
]}

机构：

[1] Pimpri Chinchwad Coll Engn, Pune, Maharashtra, India

来源：

PROCEEDINGS OF SEVENTH INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY, ICICT 2022, VOL. 2 | 2023年 / 448卷

关键词：

Data pre-processing; Classification; Concept drift detection; Concept adaptation;

D O I：

10.1007/978-981-19-1610-6_63

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Online reviews have a great impact on the e-commerce industry. Online users are free to post their perspective on products, which might not always be unbiased or accurate. Such unbiased reviews from the customers can affect both buyers and sellers in the industry. The details of this paper focus on a fake review detection system. This paper examines different techniques used in fake review detection which involves data pre-processing to pre-process and extract features from rawdata, classification to classify review as fake or real. Also, our study deals with drift in data, its detection methods, as well as drift adaptation strategies.

引用

页码：719 / 726

页数：8

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