Results 251 to 260 of about 346,740 (322)

Fake Customer Review Detection System

International Journal of Innovative Research in Engineering, 2023
Online purchasing is rising bit by bit since each service or product is easily accessible. Sellers are obtaining more reaction to one’s corporation factors. Several people generally frustrated kinds of persons misdirect others by sharing false comments to encourage or damage the image of any specific goods or services according to wish. Such people are
Abhishek Kumar Roy, Devsharan Singh, Imran Raeeni, Izmamul Ansari   +3 more
openaire   +1 more source

Building Fake Review Detection Model Based on Sentiment Intensity and PU Learning

IEEE Transactions on Neural Networks and Learning Systems, 2023
Fake review detection has the characteristics of huge stream data processing scale, unlimited data increment, dynamic change, and so on. However, the existing fake review detection methods mainly target limited and static review data.
Shunxiang Zhang, Aoqiang Zhu, Guangli Zhu, Zhongliang Wei, Kuan Ching Li   +4 more
semanticscholar   +1 more source

Towards Amazon Fake Reviewers Detection

Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications, 2020
Online marketplaces such as Amazon allow people to share their experiences about purchased products using textual comments known as product reviews. These reviews have become a common tool that users rely on to get insights on the quality and functionality of products and services from online consumers.
Youssef Esseddiq Ouatiti, Noureddine Kerzazi   +1 more
openaire   +1 more source

Detecting fake reviews through topic modelling

Journal of Business Research, 2022
Against the uncertainty caused by the information overload in the online world, consumers can benefit greatly by reading online product reviews before making their online purchases. However, some of the reviews are written deceptively to manipulate purchasing decisions.
Şule Öztürk Birim, Ipek Kazancoglu, Sachin Kumar Mangla, Aysun Kahraman, Satish Kumar, Yigit Kazancoglu   +5 more
openaire   +2 more sources

Ensemble Learning for Detecting Fake Reviews

2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), 2020
Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews. Several machine learning-based approaches can automatically detect deceptive and fake reviews. Recently, there have been studies reporting the performance of ensemble learning-based approaches in comparison to ...
Luis Gutierrez-Espinoza, Faranak Abri, Akbar Siami Namin, Keith S. Jones, David R. W. Sears   +4 more
openaire   +1 more source

Semi-Supervised Based Bangla Fake Review Detection: A Comparative Analysis

International Congress on Information and Communication Technology
This study investigates the application of supervised and semi-supervised methods to enhance pre-trained language models for distinguishing between fake and genuine Bengali reviews, using limited annotated data.
Nurul Absar, Tanjim Mahmud, Abubokor Hanip, Mohammad Shahadat Hossain   +3 more
semanticscholar   +1 more source

Fake Review Detection using Deep Learning

2023
In recent times online shopping has evolved rapidly, but finding a quality product in such a complex network is not a simple task. Internet reviews help users to find relevant items. But there is a high magnitude of fake internet reviews available online.
Sangeetha S, Sangeetha B, Raja Kumar I, Shevannth R, Krishna Prasath S, Mohammed Rafi M   +5 more
openaire   +1 more source

Fake Review Detection System Using Machine Learning

2025 2nd International Conference on Computational Intelligence and Computing Applications (ICCICA)
Online shopping and digital platforms have increased as a result of which fake reviews have also increased, confusing customers and damaging business reputations.
Mayank Srivastava, Nitin Singh, Debashis Chakraborty   +2 more
semanticscholar   +1 more source

Fake Review Detection: Taxonomies, Benchmarks, and Intent Modeling Frameworks

Journal of Information Systems Engineering & Management
Fake reviews on digital platforms are proliferating to a great extent, which is a challenge to the integrity of digital consumer ecosystems. This has put the development of reliable detection mechanisms as a critical research priority in influencing ...
Shukla Banik, Ritam Rajak
semanticscholar   +1 more source

Fake Review Detection using Random Forest Classifier

2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS)
In recent years, online review plays crucial role in the modern consumer experience to provide the valuable insights into the performance and quality of the products and services.
Kishor Mane, Snehlata Dongre, M. Madankar   +2 more
semanticscholar   +1 more source