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A customer rating for a product for sale on Amazon.com is displayed on a computer screen on Dec. 16, 2019, in New York. One Tap reviews have made it harder to determine the reliability of star ratings. (AP Photo/Jenny Kane) He suspects the changes have helped push up the average rating on One Tap reviews to about 4.6 stars, compared with 4.3 stars when reviewers had to comment on, as well as rate, a product. "This has already led to abuse," he wrote in a blog post from February and updated in July. "We can't see the dates the ratings were submitted, which users submitted the ratings, what other products those users rated."

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The global pandemic of Covid-19 at the start of the year 2020 leaves a significant impact on everything and everyone. This outbreak shakes the world and shifts the dynamics of e-commerce and online shopping. The enforcement of lockdown and social distancing lead the world to buy products online. One of the most pressing issues faced today is fraud regarding customers' opinions on online products or services relevant to a brand or an organization [1], [2]. The matter has become more sophisticated and organized due to the profit achieved by such pursuit. This phenomenon is called "Opinion Spamming" [3], [4]. Dissimilar to other spam, opinion spam are a tad hard to detect as understanding the context is important to detect the deceptiveness of a review. These reviews are posted by people who are inexperienced with the subject, which is why they are considered spam. Given the dynamic nature of the reviews, supervised learning techniques suffer from a few limitations. [2], [5], [6]. Not until the "quality" of the review is known, a garbage-in-garbage-out [7] situation can transpire. In a study, [7] it was accentuated by the researchers that fake or genuine reviews are hard to label by humans. This complicates the search for the ground truth for given instances accurately. Due to the versatile nature of these reviews and the lack of reliable data, according to the study [8], [9] methods were utilized to detect deceptive spam. Semi-Supervised techniques were used to improve classification [1], [7], [10]–​[13]. Millions of people are delivering their ideas on social media on various products, services, and events. Along with that, social media also consists of billions of short informal texts that may include SMS, tweets, messages, emails reviews, etc. [10], [14]. This scenario has brought light upon the topic for researchers to look deep into Sentiment Analysis, Opinion Mining, and Review Analysis because these reviews are potent on any business's survival and downfall. For this reason, it is essential to detect their genuineness. As the popularity of the social web increases, multiple users will keep on spreading various kinds of content almost which lacks any trustworthy external source implying that there is no way of authenticating the content being posted [3], [15], [16]. In the business section, this phenomenon affects an individual consumer and corrupts the confidence of a purchaser in online shopping. Identifying indicators of these fraudulent reviews based on the fraudster's behavior is also an essential task. Due to this, a few scholars have utilized the techniques of Data Mining and Natural Language Processing (NLP) [8], [16], [17] and other techniques such as data cleansing and database query processing to deal with raw data. However, these techniques did not efficiently solve the spam reviews problem. Lately, the reviewers have given plenty of new reviews every day. In this manner, information cleaning and repair will prompt flood in high business activity costs. As the genuineness cannot be identified, it will not be in our interest to approve the database query process that filters those spam. Given the extensive use of social media, intense competition arises in which there is a vital role of consumer reviews which has a great impact on the online marketplace [8], [11], [18]. For improved decision-making, people and organizations need to improve decision-making before purchasing any product [9], [19], [20]. Writing fraudulent comments is mostly done by professionals the establishments hire. These professionals are paid for which they post negative and positive comments on products or brands that are a major help in uplifting or defaming a targeted business [3]. However, these actions of a user could also end up being only a coincidence. One of the principal issues we are confronting today is detecting fake reviews and the extraction of genuine emotion in an opinion. According to American research, 80% of purchasing behavior depends on product feedback. The problem is to determine if the feedback given is genuine or fraudulent. A supervised learning technique is proposed by initially studying the nature of the dataset. We did a thorough analysis of different types of approaches that are working in the same domain. Furthermore, we proposed a technique that shows more remarkable results than state-of-the-art methodologies. Fake reviews are the most pressing issue in the present era. It is one of the most intense topics because it impacts the business world considerably. The gain and loss of businesses partially depend on the feedback, especially in the e-commerce domain. Therefore, it is vital to determine their authenticity by using Machine Learning techniques such as K- Nearest Neighbor, Support Vector Machine, and Logistic regression (SKL).In a recent study,mohawesh et.al. [21] presented a survey of existing models for fake reviews detection. According to this survey, SKL algorithms outperform the accuracy for the proposed problem. The Naive Bayes algorithm is one of the best classification algorithms of machine learning. However, the accuracy of the Naive Bayes algorithm for the detection of fake reviews is slightly less than SKL algorithms [21]. The proposed system includes the following modules; Throughout this research, it has been observed that fake reviews are indeed hard to tackle. Many studies have been working on this topic, but no study has given a one hundred percent result. Even in the present era, many loopholes are not being addressed. We proposed a methodology using machine learning-based text classification that helped determine whether the given comments on a particular product/service are real or fake. Our SKL technique proved to be more robust than already existing methodologies in the same field and proved to be more accurate. The results prove that SKL based fake review provides 95% on Yelp dataset and 89.03% accuracy on TripAdvisor dataset compared to other state-of-the-art techniques. Since most of the researchers mainly focused on a complete supervised learning process. Therefore, in the future, we would like to study Positive-unlabeled (PU) learning techniques in depth, which is a semi-supervised learning approach.

6. 99.

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6. 99.