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 | Implementation towards Fake Reviews Detection for Online Product using ML |
|---|---|
| ABSTRACT | The development of e-commerce platforms has given people a new way to generate and consume a great deal of information on the web. The arrival of online e-commerce platforms, people need not run to out for the basic need of regular stuff because of their fast and efficient features. As now a day’s people are counting on online products therefore the importance of a review goes higher. For selecting a product, a customer undergo thousands of reviews to know a product, Thus Product review helps for marketing products online. Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies’ reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. The proposed system helps to detect the fake online reviews and remove it thus helping customer to review the product according to original review and ratings. In this work, reviews published are identified by searching for the particular keyword and then the polarity of review is evaluated as positive and negative. The work also identifies the Ip address of particular device , if more than three reviews are posted from same Ip address, the user is blocked. The reviews are evaluated based on feature selection of each score words. In order to select the best features Naive Bayes Classifier (NBC) is used for training and testing the features of a words and also evaluating the polarity of each review. Performance evaluation parameters such as accuracy, precision and time is taken into consideration and compared with three machine learning classifiers, namely, Random Forest, Naive Bayes and Support Vector Machine(SVM) are calculated to determine fake review. |
| AUTHOR | OMKAR HAKE, JIVAN JADHAV, ANIKET GAIKWAD, SUSHANT KALE, PUNAM RAHANGDALE, VAIDEHI KHATAVKAR Student, Department of Computer Science Engineering (CSE), JSPM University, Pune, Maharashtra, India Assistant Professor, Department of Computer Science Engineering (CSE), JSPM University, Pune, Maharashtra, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1403077 |
| pdf/77_Implementation towards Fake Reviews Detection for Online Product using ML.pdf | |
| KEYWORDS | |
| References | [1] R. Hassan and M. R. Islam, ”Detection of fake online reviews using semi-supervised and supervised learning,” 2019 International Confer- ence on Electrical, Computer and Communication Engineering (ECCE), 2019, pp. 1-5, doi: 10.1109/ECACE.2019.8679186. [2] Naveen Kumar, Deepak Venugopal, Liangfei Qiu & Subodha Kumar (2018) Detecting Review Manipulation on Online Platforms with Hi- erarchical Supervised Learning, Journal of Management Information Systems, 35:1, 350-380, DOI: 10.1080/07421222.2018.1440758 [3] Martens, D., Maalej, W. Towards understanding and detecting fake reviews in app stores. Empir Software Eng 24, 3316–3355 (2019). [4] H. Deng et al., ”Semi-Supervised Learning Based Fake Review Detec- tion,” 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), 2017, pp. 1278-1280, doi: 10.1109/ISPA/IUCC.2017.00195. [5] W. Medhat, A. Hassan, and H. Korashy, “Sentiment analysis algorithms and applications: A survey,” Ain Shams Engineering Journal, vol. 5, no. 4, pp. 1093–1113, 2014. |
