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In the era of steadily expanding e-commerce, people prefer to purchase things and commodities online which helps them to save time and efforts. The huge quantity of these online purchase decisions is influenced by the suggestions of previous purchasers thereby increasing the spam content on the websites. As of the prevalence of spam content on social media is rapidly expanding, the subscribers receive a large number of junk information such as malevolent links, bogus accounts, fraud news and reviews via social networking sites and are unable to differentiate between spam and legitimate texts thereby making spam recognition vital. This research paper discusses about the novel soft computing and ensemble machine learning techniques and challenges based on detection of these review spams. It uses the datasets from different hotels and extracted its reviews, creation of novel model to analyze the problem, thus resulting in overall performance of model approximately equal to 85% of accuracy.

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