The Performance of Machine and Deep Learning Algorithms in Detecting Fake Reviews
Publication Date
1-1-2023
Document Type
Conference Proceeding
Publication Title
Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
DOI
10.1109/BigData59044.2023.10386101
First Page
2499
Last Page
2507
Abstract
The advent of the Internet has enabled everyone with access to it to provide their views online. This freedom of expression has also resulted in an increasing amount of unstructured text data daily, which can be leveraged to build models that can help make better business decisions. Customer reviews have become an integral part of the decision-making process as there is a tremendous increase in online products and services. Reviews provided by users online have a major problem regarding reliability and authenticity. It is an arduous task to make business decisions based on unstructured reviews whose trustworthiness is not established. Hence, this paper focuses on classifying the reviews of certain restaurants available on the Internet using different machine/deep learning techniques and summarises the findings. The results show that deep learning methods are more efficient in identifying fake reviews. More specifically, combining BERT and a 4-layered Feed Forward network gave 96% accuracy in detecting fake reviews.
Funding Number
2319802
Funding Sponsor
National Science Foundation
Keywords
BiLSTM, fake reviews, LSTM, machine learning, NLP, transformers
Department
Computer Science
Recommended Citation
Bharkavi Sachithanandam, Akbar Siami Namin, and Faranak Abri. "The Performance of Machine and Deep Learning Algorithms in Detecting Fake Reviews" Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 (2023): 2499-2507. https://doi.org/10.1109/BigData59044.2023.10386101
