• Sonali Dixit
• Suraj Prakash Padalkar
• Dr. Vinod Wadne
• Vaishnavi Subhash Jogdand
• Murfahad Aslam Shikalgar

People usually trust the evaluations and recommendations given on products while purchasing one. Ratings or Review of a service can produce a fantastic impression on a product profile such as its company name and brands. Organizations must survey opinions and outcomes that come over its products. On other side, its very difficult to trace and structure popular review in organized way. Various opinions on the internet are involving many efforts to manually process. The term "system" refers to a set of rules created that can spontaneously classify positive and negative opinions which will help them to investigate a product’s growth in the case to consistency, efficiency, and things that needs to improve will eventually give better path to improvise the product. Customer’s both good and poor ratings and reviews are critical in evaluating customer requests and receiving product input. Natural Language Processing (NLP)in which analyzing sentiments gathers contextual data like positive, negative and neutral. This research looks at a huge number of online mobile phone ratings. Disappointment, expectancy, disgust, apprehension, happiness, regret, surprise, and confidence were all classified into both good and bad aspects. This clearly stated type of feedbacks that supports in a thorough examination of that same output that will allow customers to make more informed selections.

Machine Learning (ML), Faux, Ratings, Reviews, Natural Language Processing, Evaluations and recommendation

"Study on Faux Review Identification Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.i415-i419, May-2022, Available :http://www.jetir.org/papers/JETIR2205962.pdf

"Study on Faux Review Identification Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppi415-i419, May-2022, Available at : http://www.jetir.org/papers/JETIR2205962.pdf