International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Fake Reviews Detection using Supervised Machine Learning Techniques |
|---|---|
| ABSTRACT | Online review systems have become an integral component of modern digital marketplaces, shaping consumer perceptions and strongly influencing purchasing decisions. Platforms such as Yelp and Amazon rely heavily on user-generated content to establish trust, credibility, and transparency between consumers and businesses. However, the growing economic and reputational impact of online reviews has also encouraged malicious behaviours, particularly the generation of deceptive or fake reviews intended to manipulate public opinion. Detecting such reviews is a challenging yet essential task within the fields of natural language processing (NLP) and machine learning. This study presents an expanded and comprehensive supervised machine learning framework for detecting fake reviews using textual data and limited metadata. The proposed approach evaluates multiple classical machine learning algorithms—including Naive Bayes, Decision Trees, Random Forests, Support Vector Machines (SVM), Gradient Boosting, K-Nearest Neighbors (KNN), and Multilayer Perceptron (MLP)—on a labelled Yelp-like dataset. Reviews are pre-processed using standard NLP techniques and represented using a bag-of-words model via Count Vectorizer |
| AUTHOR | S. VENKATESAN, K. MADHAVI , V. GREESHMA, B. RUTHIKA, K. DIVYA, K. MADHUSUDHAN, V. KARTHIK Associate Professor, Department of CSE (Data Science), NSRIT, Vishakhapatnam, India Student of Department of CSE (Data Science), NSRIT, Vishakhapatnam, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403061 |
| pdf/61_Fake Reviews Detection using Supervised.pdf | |
| KEYWORDS | |
| References | 1. Sharma, R., & Gupta, P. (2025). Cross-platform Fake Review Detection: A Comparative Analysis of Supervised and Deep Learning Models. International Journal of Information Technology and Computer Science. 2. Kumar, A., & Singh, R. (2025). Deep Learning Models for Fake Review Detection using BERT and BiLSTM. International Journal of System Assurance Engineering and Management. 3. Zhang, Y., & Lee, K. (2024). Fake Review Detection using Transformer-based Enhanced LSTM and RoBERTa. Journal of Data Science and Analytics. 4. Chen, X., & Zhao, L. (2024). Detecting Fake Online Reviews using Unsupervised Methods. Journal of Information Systems.https://www.sciencedirect.com/ 5. Reddy, S., & Prakash, M. (2025). AI-Generated Product Review Detection in Dravidian Languages using Transformer Models. arXiv preprint arXiv:2503.09289. 6. Wang, H., & Liu, Y. (2023). Semi-supervised GAN for Fake Review Detection. arXiv preprint arXiv:2304.02739. 7. Johnson, T., & Miller, D. (2025). Large Language Models as Hidden Persuaders: Challenges in Detecting AI-Generated Reviews. arXiv preprint arXiv:2506.13313.https://arxiv.org/abs/2506.13313 |
