Monitoring of Fake Online Reviews Through Machine Learning

Registration ID: IJNRD_196283

Published ID: IJNRD2305583

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Vijendra S.N , Vaishnavi N. Patil, Amrutha B. M, Jyothi R., Vandana V.

Fake reviews detection, Machine learning Algorithms, Text Classification, Natural Language Processing, Bigrams, Term Frequency and Inverse Document Frequency.

The escalating trend of online shopping has witnessed a significant surge, with a growing number of individuals preferring to procure products from virtual stores. Consequently, the utilization of the Internet and online marketing has attained widespread popularity. The online marketplace now offers an extensive array of millions of products and services, thereby generating a vast volume of information. Consequently, locating the most suitable services or products that align with specific requirements has become a daunting task. In such a vast landscape, customers heavily rely on reviews and opinions shared by others, which are based on their personal experiences, to make informed decisions. The spread of bogus reviews has emerged as a critical concern in today's world of intense competition. Companies are increasingly turning to employing people to create positive reviews for their own goods or services while writing unfairly critical comments about their rivals, which is a worrying trend. These dishonest actions have serious repercussions because they deceive prospective buyers who rely on these ratings to make wise purchasing decisions. A strong system that is capable of identifying and removing bogus reviews is thus urgently needed. In order to identify false reviews, this study will examine a variety of supervised, unsupervised, and semi-supervised machine learning algorithms. Multiple classifiers, including Support Vector Machine (SVM), Nave Bayes, and Random Forest Classifier, are used to assess each approach. Additionally, the study takes into consideration the effectiveness of n-gram techniques and linguistic models to enhance the evaluation process. The proposed methodology demonstrates a noteworthy accuracy rate of 87% in effectively identifying fake reviews.

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Vijendra S.N , Vaishnavi N. Patil, Amrutha B. M, Jyothi R., & Vandana V. (May-2023). Monitoring of Fake Online Reviews Through Machine Learning . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), f577-f584. https://ijnrd.org/papers/IJNRD2305583.pdf

Volume 8 Issue 5, May-2023

Pages : f577-f584

Paper Reg. ID: IJNRD_196283

Published Paper Id: IJNRD2305583

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Bangalore, Karnataka , India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305583.pdf

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