Fake Product Review Detection and Elimination using Opinion Mining

Identification and elimination of fake reviews and their removal from the dataset provided using the supervised machine learning algorithm and natural language processing techniques based on a vast variety of aspects. In this proposed paper, we trained the counterfeit review dataset by the process of using two independently developed machine learning algorithm models for assessing the extent to which the information being provided is real. The counterfeit product evaluations can be found on numerous online retailers are mostly influencing the customers to buy those products and profit for those products is probably dependent on the reviews of those products. Hence these counterfeit reviews must be noticed so that large E-commerce companies like Meesho, Amazon, Flipkart, Nykaa, etc. can address this issue so that fraudsters and fraudulent critics are taken out, sustaining users’ credibility in shopping sites. This approach may be utilized for websites and apps with relatively few consumers, estimating the authenticity of reviews so that online businesses can respond to them suitably. This model is developed using Naive Bayes, Support Vector Machine,and TF-IDF (term frequency-inverse document frequency)Vectorizer. To detect spam reviews on a website or application instantly, one can make use of these models. However, effectively countering spammers requires a sophisticated model that has to undergo training on a large dataset of millions of reviews. In this work” Reviews of 20 Hotels in Chicago hotel dataset” a limited dataset is utilized to train the models on a small scale, but it can be expanded to achieve greater accuracy and authenticity in the reviews.