• Alamanda Sreevani
• Dr B.Prajna

Online reviews are very important in decision making of customer whether to purchase a product or service. These are main source of information getting from the past customer experience about the features of that service which we are going to purchase. Now a days’ Internet is no longer use only for communication purpose. Its use is spread over wide variety of applications’ and E-Commerce is one of them. The most important part in e-commerce, from consumer perspective is, the reviews associated with products. Most of the people do their decision making, based on these online reviews about products or services. These reviews not only help user to know the product or service thoroughly but also affect user’s decision making ability to a great extent and also divert the sentiments about the product positively or negatively. As a result, there have been attempts made, to change the product sentiments positively or negatively by manipulating the online reviews artificially to gain the business benefits. Ultimately, affect the genuine business experience of the user. Therefore in this paper, we have dealt with this particular problem of ecommerce field, specifically online reviews’ in particular and sentiment analysis domain as a whole, in general. This paper introduces some machine learning techniques like Support Vector Machine and Random Forests for sentiment classification of reviews and to detect fake online reviews using the data set of a Hotel reviews. Sentiment Analysis has become most interesting in analysis of text. Using sentiment analysis we can separate negative and positive reviews as well. This paper introduces some semi-supervised and supervised text mining models to detect fake online reviews as well as compares the efficiency of both techniques on dataset containing hotel reviews.

Fake Online reviews, Semi- supervised learning, supervised learning, Expectation Maximization algorithm, Random Forests, Support Vector Machine classifier

"Predicting Fake online Reviews using Machine Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 11, page no.369-376, November-2020, Available :http://www.jetir.org/papers/JETIR2011183.pdf

"Predicting Fake online Reviews using Machine Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 11, page no. pp369-376, November-2020, Available at : http://www.jetir.org/papers/JETIR2011183.pdf