Abstract
Internet reviews may affect a customer’s selection, and they can assess a product by correlating it to other brands. If the reviews are authentic, the customer may only pick a product that fulfils their demands. If the reviews, on the other hand, are phoney, the buyer is duped. It is vital to obtain the identification of fake consumer opinions in order to address this issue. To evaluate if a review is fraudulent or not, the actions of reviewers are extracted based on a semantic analysis of his review content. In this study, a data set for a mixed product was retrieved from the web, along with reviews and other information about the reviewers, to identify false reviewers using four algorithms: Support Vector Machine, Logistic Regression, K-Nearest Neighbour and Decision Tree. The accuracy rate along with precision rate of the aforementioned four methods are used to validate the significance of the features on the choice. Experiments were carried out on a large number of reviews gathered from the internet, demonstrating the efficacy of the proposed method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Crawford, M., Khoshgoftaar, T.M., Prusa, J.D., Richter, A.N., Al Najada, H.: Survey of review spam detection using machine learning techniques. J. Big Data 2(1), 1–24 (2015). https://doi.org/10.1186/s40537-015-0029-9
Danti, A., Sanjay, K.S.: Detection of fake opinions on online products using decision tree and information gain. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 372–375, 27 March 2019
Chauhan, S., Anupam, G., Prafull, G., Avishkar, C., Mahendra, G.: Research on product review analysis and spam review detection, pp. 390–393 (2017). https://doi.org/10.1109/SPIN.2017.8049980
Elmurngi, E., Gherbi, A.: Fake reviews detection on movie reviews through sentiment analysis using supervised learning techniques. Int. J. Adv. Syst. Meas. 11(1&2), 196–207 (2018)
Adike, M.R., Reddy, V.S.: Detection of Fake Review and Brand Spam Using Data Mining Technique (2016)
Tewari, A., Jangale, S.: Spam filtering methods and machine learning algorithm-a survey. Int. J. Comput. Appl. 154, 8–12 (2016). https://doi.org/10.5120/ijca2016912153
Wahyuni, E., Djunaidy, A.: Fake review detection from a product review using modified method of iterative computation framework. In: MATEC Web of Conferences, vol. 58 (2016). https://doi.org/10.1051/matecconf/20165803003
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Krishnan, H.M., Preetha, J., Shona, S.P., Sivakami, A. (2022). Detection of Fake Reviews on Online Products Using Machine Learning Algorithms. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-96299-9_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96298-2
Online ISBN: 978-3-030-96299-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)