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Introduction 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; Parts-of-Speech tagging Fake reviews detection techniques are widely used in the e-commerce domain, which plays an essential role in our economy as they can easily uplift or defame a product company or service. Since the purchase decision is firmly motivated by the reviews or ratings, the study shows that work has been concluded in detecting these fraudulent reviews, but spammers' demeanor is constantly developing. Spammers have been discreetly designing these fake reviews to camouflage their malevolent intentions. Many businesses appoint professionals who write inappropriate positive and negative reviews for financial gains. These are fabricated comments that these professionals intentionally write for the sake of seeming authentic. Fake reviews have a powerful impact as they directly influence customers' decision-making power. Relying on the feedback, customers either reject the product or decide to buy the product. Fake reviews are fictitious comments that are either machine-generated or user-generated. Both spams are challenging to identify. In the ongoing years, the use of e-commerce has increased drastically. There have been chances of fraudulent comments that play an essential role in defaming or uplifting a business. Due to the intense competition between organizations, it has become more sophisticated, and thus, many of them use the wrong approach to receive potential profit. Reviews on a product play a part in consumer decisions, and they build confidence in that particular product. However, they cannot be sure about the fallacy of these reviews. Fake reviews can either be deceptive or destructive. Destructive spams are easier to identify by a typical customer since they are non-review and contain ads and messages unrelated to the product. The latter, however, may contain sentimental reviews that may be positive or negative and, thus, problematic. Deceptive review, However, considering the deceptiveness of these reviews, these fake reviews are being used to advance a business or tattle and harm the repute of businesses that are in great competition. The existence of such reviews is crucial for the customer and the business. This concept is known as "Opinion Mining." To bring out the public's mood, Natural Language Processing is used regarding a specific product, service, or company. Untruthful Opinions are negative opinions given to damage the company's repute specifically or promote a business undeservingly. Likewise, positive opinions are given for an organization to gain fame inappropriately. Brand-specific reviews primarily target brands, get negative or positive reviews. Advertisements and irrelevant reviews have no meaning and compromise of no opinion at all. Since the purchase decision is firmly motivated by the reviews or ratings, the study shows that work has been concluded in detecting these fraudulent reviews, but spammers' demeanor is constantly developing. Spammers have been discreetly designing these fake reviews to camouflage their malevolent intentions. Many businesses appoint professionals who write inappropriate positive and negative reviews for financial gains. These are fabricated comments that these professionals intentionally write for the sake of seeming authentic. Fake reviews have a powerful impact as it directly influences the customer's decision-making power. Relying on the feedback, customers either reject the product or decide to buy the product. The product's price is a significant factor for a consumer, but feedback or reviews on those products are also considered seriously when purchasing something online. Building a trust factor is essential because most people rely on feedback to make a purchase online. The paper is arranged in sections as sections II covers literature review, the proposed methodology is explained in section III, section IV comprises design and implementation including results. A conclusion has been drawn in section V. Literature Review Conclusion and Future Work ACKNOWLEDGMENT crossbody bags cyber monday

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