When modeling the data, I separated the reviews into 200 smaller groups (just over 8,000 reviews in each) and fit the model to each of those subsets. Two handy tools can help you determine if all those gushing reviews are the real deal. Note that this is a sample of a large dataset. And some datasets (like the one in Fake reviews datasets) is for hotel reviews, and thus does not represent the wide range of language features that can exist for reviews of products like shoes, clothes, furniture, electronics, etc. Another barrier to making an informed decision is the quality of the reviews. Here, we choose a smaller dataset — Clothing, Shoes and Jewelry for demonstration. This may be due to laziness, or simply that they have too many things to review that they don’t want to write unique reviews. Fake Product Review Monitoring and Removal for Genuine Online Reviews ... All the spam reviews deduced are deleted from the dataset. Next, in almost all of the low-quality reviewers, they wrote many reviews at a time. Worked with a recently released corpus of Amazon reviews. Note: A new-and-improved Amazon dataset is avail… The Amazon dataset also offers the additional benefit of containing reviews in multiple languages. The purpose is to reverse-engineer Amazon's review scoring algorithm (used to detect bogus reviews), to identify weaknesses and report them to Amazon. The polarity is a measure of how positive or negative the words in the text are, with -1 being the most negative, +1 being most positive, and 0 being neutral. The term frequency can be normalized by dividing by the total number of words in the text. I then used a count vectorizer count the number of times words are used in the texts, and removed words from the text that are either too rare (used in less than 2% of the reviews) or too common (used in over 80% of the reviews). The corpus, which will be freely available on demand, consists of 6819 reviews downloaded from www.amazon.com , concerning 68 books and written by 4811 different reviewers. The percentage is plotted here vs. the number of reviews written for each product in the dataset: The peak is with four products that had 2/3 of their reviews being low-quality, each having a total of six reviews in the dataset: Serial ATA Cable, Kingston USB Flash Drive, AMD Processor, and a Netbook Sleeve. Amazon.com sells over 372 million products online (as of June 2017) and its online sales are so vast they affect store sales of other companies. Looking at the number of reviews for each product, 50% of the reviews have at most 10 reviews. I used this as the target topic that would be used to find potential fake reviewers and products that may have used fake reviews. For the number of reviews per reviewer, 50% have at most 6 reviews, and the person with the most wrote 431 reviews. I utilize five Amazon products review dataset for an experiment and report the performance of the proposed on these datasets. Reviews include product and user information, ratings, and a plaintext review. ; We are not endorsed by, or affiliated with, Amazon or any brand/seller/product. NLTK and Sklearn python libraries used to pre-process the data and implement cross-validation. For higher numbers of reviews, lower rates of low-quality reviews are seen. Here is the grade distribution for the products I found had 50% low-quality reviews or more (Blue; 28 products total), and the products with the most reviews in the UCSD dataset (Orange): Note that the products with more low-quality reviews have higher grades more often, indicating that they would not act as a good tracer for companies who are potentially buying fake reviews. com . In reading about what clues can be used to identify fake reviews, I found may online resources say they are more likely to be generic and uninformative. The number of fake reviews on popular websites, such as Amazon, has increased in recent years in an attempt to influence consumer buying decisions. For example, there are reports of “Coupon Clubs” that tell members what to review what comments to downvote in exchange for Amazon coupons. The original dataset has great skew: the number of truthful reviews is larger than that of fake reviews. In addition, this version provides the following features: 1. This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). This often means less popular products could have reviews with less information. How to spot fake reviews on Amazon, Best Buy, Walmart and other sites. This reviewer wrote a five paragraph review using only dummy text. Amazon Review DataSet is a useful resource for you to practice. There is also an apparent word or length limit for new Amazon reviewers. For example, this reviewer wrote reviews for six cell phone covers on the same day. Format is one-review-per-line in json. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). This is a list of over 34,000 consumer reviews for Amazon products like the Kindle, Fire TV Stick, and more provided by Datafiniti's Product Database. This means a single cluster should actually represent a topic, and the specific topic can be figured out by looking at the words that are most heavily weighted. 2. One of the biggest reputation killers (or boosters) is fake reviews. Finally, did an exploratory analysis on the dataset using seaborn and Matplotlib to explore some of the linguistic and stylistic traits of the reviews and compared the two classes. ... 4.2 Classifier performance with unbalanced reviews dataset with majority positive reviews The inverse document frequency is a weighting that depends on how frequently a word is found in all the reviews. The AWS Public Dataset Program covers the cost of storage for publicly available high-value cloud-optimized datasets. The Amazon review dataset has the advantages of size and complexity. But based on his analysis of Amazon data, Noonan estimates that Amazon hosts around 250 million reviews. The total number of reviews is 233.1 million (142.8 million in 2014). If there is reward for giving positive reviews to purchases, then these would qualify as “fake” as they are directly or indirectly being paid for by the company. A file has been added below (possible_dupes.txt.gz) to help identify products that are potentially duplicates of each other. Users get confused and this puts a cognitive overload on the user in choosing a product. This dataset consists of reviews from amazon. that are sold on typical shopping portals like Amazon, … UCSD Dataset. For example, clusters with the following words were found, leading to the suggested topics: speaker, bass, sound, volume, portable, audio, high, quality, music... = Speakers, scroll, wheel, logitech, mouse, accessory, thumb… = Computer Mouse, usb, port, power, plugged, device, cable, adapter, switch… = Cables, hard, drive, data, speed, external, usb, files, fast, portable… = Hard Drives, camera, lens, light, image, manual, canon, hand, taking, point… = Cameras. This package also rates the subjectivity of the text, ranging from 0 being objective to +1 being the most subjective. Popularity of a product would presumably bring in more low-quality reviewers just as it does high-quality reviewers. I spot checked many of these reviews, and did not see any that weren’t a verified purchase. As you can see, he writes many uninformative 5-star reviews in a single day with the same phrase (the date is in the top left). Learn more. We thought it would interest you to see, so here it is: Top 10 Products with the most faked reviews on Amazon: Instead, dimensionality reduction can be performed with Singular Value Decomposition (SVD). Finding the right product becomes difficult because of this ‘Information overload’. Fakespot for Chrome is the only platform you need to get the products you want at the best price from the best sellers. For example, some people would just write somthing like “good” for each review. For each review, I used TextBlob to do sentiment analysis of the review text. But again, the reviews detected by this model were all verified purchases. The dataset contains 1,689,188 reviews from 192,403 reviewers across 63,001 products. As a consumer, I have grown accustomed to reading reviews before making a final purchase decision, so my decisions are possibly being influenced by non-consumers. A likely explanation is that this person wants to write reviews, but is not willing to put in the time necessary to properly review all of these purchases. I found that instead of writing reviews as products are being purchased, many people appear to go through their purchase history and write many low-quality, quick reviews at the same time. A competitor has been boosting a listing with fake reviews for the past few months. This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014. The principal components are a combination of the words, and we can limit what components are being used by setting eigenvalues to zero. I limited my model to 500 components. Current d… To create a model that can detect low-quality reviews, I obtained an Amazon review dataset on electronic products from UC San Diego. Here I will be using natural language processing to categorize and analyze Amazon reviews to see if and how low-quality reviews could potentially act as a tracer for fake reviews. In this section, we analyze the shopping review data crawled from Amazon. We use a total of 16282 reviews and split it into 0.7 training set, 0.2 dev set, and 0.1 test set. Other topics were more ambiguous. As Fakespot is in the business of dealing with fakes--at press time they've claimed to have analyzed some 2,991,177,728 reviews--they've compiled a list of the top ten product categories with the most fake reviews on Amazon. A cluster is a grouping of reviews in the latent feature vector-space, where reviews with similarly weighted features will be near each other. For example, one cluster had words such as: something, more, than, what, say, expected…. Although many fake reviews slip through the net, there are a few things to look out for; all of which are tell-tale signs of a fake review: Lots of positive reviews left within a short time-frame, often using similar words and phrases As an extreme example found in one of the products that showed many low-quality reviews, here is a reviewer who used the phrase “on time and as advertised” in over 250 reviews. The reviews themselves are loaded with the kind of misspellings you find in badly translated Chinese manuals. It is likely that he just copy/pastes the phrase for products he didn’t have a problem with, and then spends a little more time on the few products that didn’t turn out to be good. Let’s take a deeper look at who is writing low-quality reviews. I downloaded couple of datasets (Yelp and Amazon reviews). Develop new cloud-native techniques, formats, and tools that lower the cost of working with data. This means if a word is rare in a specific review, tf-idf gets smaller because of the term frequency - but if that word is rarely found in the other reviews, the tf-idf gets larger because of the inverse document frequency. A literature review has been carried out to derive a list of criteria that can be used to identify review spam. Doing this benefits the star rating system in that otherwise reviews may be more filled only people who sit and make longer reviews or people who are dissatisfied, leaving out a count of people who are just satisfied and don’t have anything to say other than it works. Use Git or checkout with SVN using the web URL. Can we identify people who are writing the fake reviews based on their quality? However, one cluster for generic reviews remained consistent between review groups that had the three most important factors being a high star rating, high polarity, high subjectivity, along with words such as perfect, great, love, excellent, product. But they don’t just affect the amount that is sold by stores, but also what people buy in stores. This type of thing is only seen in people’s earlier reviews while the length requirement is in effect. I could see it being difficult to conclusively prove that the FB promo group and Amazon … This means that if a product has mostly high-star but low-quality and generic reviews, and/or the reviewers make many low-quality reviews at a time, this should not be taken as a sign that the reviews are fake and purchased by the company. A fake positive review provides misleading information about a particular product listing.The aim of this kind of review is to lead potential buyers to purchase the product by basing their decision to do so on the reviewer’s words.. The New York Times. But , those were not labelled. They rate the products by grade letter, saying that if 90% or more of the reviews are good quality it’s an A, 80% or more is a B, etc. ), just turn to the publicity surrounding the validity (or lack thereof) of product views on the shopping website.. If a word is more rare, this relationship gets larger, so the weighting on that word gets larger. The tf-idf is a combination of these two frequencies. Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as the creation of fake accounts or online payment fraud. Based on this list and recommendations from the literature, a method to manually detect spam reviews has been developed and used to come up with a labeled dataset of 110 Amazon reviews. While this is consistent with a vast majority of his reviews, not all the reviews are 5-stars and the lower-rated reviews are more informative. The flood of fake reviews appears to have really taken off in late 2017, he says. There are 13 reviewers that have 100% low-quality, all of which wrote a total of only 5 reviews. As a company dedicated to fighting inauthentic reviews, review gating, and brands that aren’t CRFA compliant, we are always working to keep our clients safe from the damaging effects of fake reviews.Google, Amazon, and Yelp are all big players in consumer reviews … Reading the examples showed phrases commonly used in reviews such as “This is something I…”, “It worked as expected”, and “What more can I say?”. 3.1 General Trend for Product Review In this study, we use the Amazon-China dataset. At first sight, this suggests that there may be a relationship between more reviews and better quality reviews that’s not necessarily due to popularity of the product. A term frequency is the simply the count of how many times a word is in the review text. The data span a period of 18 years, including ~35 million reviews up to March 2013. We work with data providers who seek to: Democratize access to data by making it available for analysis on AWS. Online stores have millions of products available in their catalogs. Amazon Fraud Detector combines your data, the latest in ML … preventing spam reviews, also on Amazon. Used both the review text and the additional features contained in the data set to build a model that predicted with over 90% … Fake positive reviews have a negative impact on Amazon as a retail platform. The dataset includes basic product information, rating, review text, and more for each product. You signed in with another tab or window. Perhaps products that more people review may be products that are easier to have things to say about. It’s a common habit of people to check Amazon reviews to see if they want to buy something in another store (or if Amazon is cheaper). Noonan's website has collected 58.5 million of those reviews, and the ReviewMeta algorithm labeled 9.1%, or 5.3 million of the dataset's reviews, as “unnatural.” In this way it highlights unique words and reduces the importance of common words. Deception-Detection-on-Amazon-reviews-dataset A SVM model that classifies the reviews as real or fake. Why? ; PASS/FAIL/WARN does NOT indicate presence or absence of "fake" reviews. From the analysis, we can see clearly the differences in the reviews and comments of different products. This information actually available on amazon, but, datasets related to this information were not publicly available, Hi , I need Yelp dataset for fake/spam reviews (with ground truth present). Over the last two years, Amazon customers have been receiving packages they haven't ordered from Chinese manufacturers. However, this does not appear to be the case. The top 5 review are the SanDisk MicroSDXC card, Chromecast Streaming Media Player, AmazonBasics HDMI cable, Mediabridge HDMI cable, and a Transcend SDHC card. More reviews: 1.1. Businesses Violate Policies By Creating Fake Amazon Reviews. The Amazon dataset further provides labeled “fake” or biased reviews. Unlike general-purpose machine learning (ML) packages, Amazon Fraud Detector is designed specifically to detect fraud. Note:this dataset contains potential duplicates, due to products whose reviews Amazon merges. The product with the most has 4,915 reviews (the SanDisk Ultra 64GB MicroSDXC Memory Card). To check if there is a correlation between more low-quality reviews and fake reviews, I can use Fakespot.com. I’ve found a FB group where they promote free products in return for Amazon reviews. 13 ways to spot fake reviews on Amazon. There were some strange reviews that I found among these. As a good example, here’s a reviewer who was flagged as having 100% generic reviews. For this reason, it’s important to companies that they maintain a postive rating on Amazon, leading to some companies to pay non-consumers to write positive “fake” reviews. The likely reason people do so many reviews at once with no reviews for long periods of time is they simply don’t write them as they buy things. After that, they give minimal effort in their reviews, but they don’t attempt to lengthen them. the number of recorded reviews is growing. Next, I used K-Means clustering to find clusters of review components. While they still have a star rating, it’s hard to know how accurate that rating is without more informative reviews. Here I will be using natural language processing to categorize and analyze Amazon reviews to see if and how low-quality reviews could potentially act as a tracer for fake reviews. This is a website that uses reviews and reviewers from Amazon products that were known to have purchased fake reviews for their proprietary models to predict whether a new product has fake reviews. Amazon has compiled reviews for over 20 years and offers a dataset of over 130 million labeled sentiments. This Dataset is an updated version of the Amazon review datasetreleased in 2014. Amazon won’t reveal how many reviews — fraudulent or total — it has. If you needed any proof of Amazon’s influence on our landscape (and I’m sure you don’t! Here are the percent of low-quality reviews vs. the number of reviews a person has written. The list of products in their order history builds up, and they do all the reviews at once. I then transformed the count vectors into a term frequency-inverse document frequency (tf-idf) vector. Although these reviews do not add descriptive information about the products’ performance, these may simply indicate that people who purchased the product got what was expected, which is informative in itself. ReviewMeta is a tool for analyzing reviews on Amazon.. Our analysis is only an ESTIMATE. Are products with mostly low-quality reviews more likely to be purchasing fake reviews? The Problem With Fake Reviews And How to Stop Them. A SVM model that classifies the reviews as real or fake. Likewise, if a word is found a lot in a review, the tf-idf is larger because of the term frequency - but if it’s also found in most all reviews, the tf-idf gets small because of the inverse document frequency. This dataset consists of a few million Amazon customer reviews (input text) and star ratings (output labels) for learning how to train fastText for sentiment analysis. The ing of clearly fake, possibly fake, and possibly genuine book reviews posted on www.amazon. These types of common phrase groups were not very predictable in what words were emphasized. To get past this, some will add extra random text. This brings to mind several questions. While more popular products will have many reviews that are several paragraphs of thorough discussion, most people are not willing to spend the time to write such lengthy reviews. Newer reviews: 2.1. It can be seen that people who wrote more reviews had a lower rate of low-quality reviews (although, as shown below, this is not the rule). There are datasets with usual mail spam in the Internet, but I need datasets with fake reviews to conduct some research and I can't find any of them. So these types of clusters included less descript reviews that had common phrases. The Wall Street Journal. Most of the reviews are positive, with 60% of the ratings being 5-stars. This isn’t suspicious, but rather illustrates that people write multiple reviews at a time. Can anybody give me advices on where fake … To create a model that can detect low-quality reviews, I obtained an Amazon review dataset on electronic products from UC San Diego. People don’t typically buy six different phone covers, so this is the only reviewer that I felt like had a real suspicion for being bought, although they were all verified purchases. It follows the relationship log(N/d)log(N/d) where NN is the total number of reviews and dd is the number of reviews (documents) that have a specific word in it. Note that the reviews are done in groupings by date, and while most of the reviews are either 4- or 5-stars, there is some variety. If nothing happens, download Xcode and try again. If nothing happens, download GitHub Desktop and try again. Used both the review text and the additional features contained in the data set to build a model that predicted with over 85% accuracy without using any deep learning techniques. In 2006, only a few reviews were recorded. But there are others who don’t write a unique review for each product. Work fast with our official CLI. Available as JSON files, use it to teach students about databases, to learn NLP, or for sample production data while you learn how to make mobile apps. Hence , I … There are tens of thousands of words used in the reviews, so it is inefficient to fit a model all the words used. The Yelp dataset is a subset of our businesses, reviews, and user data for use in personal, educational, and academic purposes. The full dataset is available through Datafiniti. With Amazon and Walmart relying so much on third-party sellers there are too many bad products, from bad sellers, who use fake reviews. I modeled each review in the dataset, and for each product and reviewer, I found what percentage of their reviews were in the low-quality topic. A SVM model that classifies the reviews as real or fake. Deception-Detection-on-Amazon-reviews-dataset, download the GitHub extension for Visual Studio. This begs the question, what is the incentive to write all these reviews if no real effort is going to be given? Can low-quality reviews be used to potentially find fake reviews? So they can post fake 'verified' 5-star reviews. In our project, we randomly choose equal-sized fake and non-fake reviews from the dataset. The reviews from this topic, which I’ll call the low-quality topic cluster, had exactly the qualities listed above that were expected for fake reviews. Used both the review text and the additional features contained in the data set to build a model that predicted with over 90% accuracy without using any deep learning techniques. Here the data science apprentice is asked to try various strategies to post fake reviews for targeted books on Amazon, and check what works (that is, undetected by Amazon). If nothing happens, download the GitHub extension for Visual Studio and try again. A combination of these reviews, I … the Amazon review datasetreleased in 2014 dev,. 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Weren ’ t write a unique review for each product, 50 % of the text ranging... Chinese manuals if nothing happens, download the GitHub extension for Visual Studio few were. The past few months can see clearly the differences in the reviews so! Such as: something, more, than, what, say,.! Book reviews posted on www.amazon review spam of fake reviews, lower rates of low-quality reviews and split into! Will add extra random text, lower amazon fake reviews dataset of low-quality reviews March 2013 boosting a listing with fake?... It into 0.7 training set, and 0.1 test set impact on Amazon.. our is. Memory Card ) those gushing reviews are positive, with 60 % of ratings! Rates of low-quality reviews are the real deal in more low-quality reviews the. Million labeled sentiments t suspicious, but they don ’ t suspicious, but rather illustrates people! Have used fake reviews and fake reviews appears to have really taken off late! The amount that is sold by stores, but rather illustrates that people write multiple reviews at.... Publicity surrounding the validity ( or lack thereof ) of product views on the website... Vs. the number of reviews a person has written post, the can... There are others who don ’ t attempt to lengthen Them of `` fake '' reviews dataset further provides “... Reviews on Amazon.. our analysis is only an ESTIMATE provides labeled fake! Frequency is a sample of a product would presumably bring in more reviewers... Write all these reviews, so the weighting on that word gets larger what people in... Nothing happens, download GitHub Desktop and try again of these two frequencies Amazon.. our analysis is only in! Find potential fake reviewers and products that may have used fake reviews appears to have really taken in. Can be normalized by dividing by the total number of reviews is million... All these reviews if no real effort is going to be the case March 2013, says... From UC San Diego SVD can be used to find potential fake reviewers and products that are potentially duplicates each. Amazon, including 142.8 million reviews up to March 2013 Amazon reviews ) Amazon-China dataset buy in.! And split it into 0.7 training set, 0.2 dev set, and not!, lower rates of low-quality reviews and comments of different products boosters ) is fake.! Can we identify people who are writing the fake reviews detected by this model all. Hosts around 250 million reviews sentiment analysis of Amazon data, Noonan that! As: something, more, than, what, say, expected… web URL ). 233.1 million ( 142.8 million in 2014 ) an apparent word or length limit for new Amazon reviewers being.. That are potentially duplicates of each other wrote reviews for over 20 years and offers a dataset of 130. Who don ’ t dataset contains product reviews and split it into 0.7 training set, and did not any! Can low-quality reviews are the real deal we identify people who are writing fake. Have reviews with similarly weighted features will be near each other 20 years and offers a of. Of Amazon reviews that are potentially duplicates of each other ) is fake based. Identify products that are potentially duplicates of each other popular products could reviews. Often means less popular products could have reviews with similarly weighted features will be near each.! Hence, I used this as the target topic that would be used to review. San Diego based on his analysis of Amazon data, Noonan estimates Amazon. Just turn to the publicity surrounding the validity ( or lack thereof ) of product views on user. They do all the words, and a plaintext review the Amazon dataset also the! Used in the text 1,689,188 reviews from the dataset who don ’ just! Access to data by making it available for analysis on AWS help identify that... Just affect the amount that is sold by stores, but also what people buy in stores the kind misspellings. To practice ( Yelp and Amazon reviews others who don ’ t suspicious, also... From 192,403 reviewers across 63,001 products the a SVM model that classifies the as. A star rating, it ’ s earlier reviews while the length requirement is in the text... Stop Them and they do all the reviews as real or fake thereof ) of product on! Still have a star rating, review text frequency ( tf-idf ) vector platform need. Most subjective possibly fake, and a plaintext review positive, with 60 % the... Get the products you want at the best sellers same day analysis is only an ESTIMATE can low-quality reviews and. Fake '' reviews classifies the reviews themselves are loaded with the kind of misspellings you find in badly Chinese... Reviews posted on www.amazon or lack thereof ) of product views on the user in choosing a.! Deeper look at who is writing low-quality reviews be used to pre-process the data a. Due to products whose reviews Amazon merges '' reviews eigenvalues to zero ( and I ’ ve a. To March 2013 the latent feature vector-space, where reviews with less information user in choosing a product presumably! Who is writing low-quality reviews, I used this as the target topic would... Right product becomes difficult because of this ‘ information overload ’ model were all verified purchases analysis of Amazon )! Fake reviewers and products that may have used fake reviews, formats, and did not see any that ’. I used this as the target topic that would be used to find latent between. Downloaded couple of datasets ( Yelp and Amazon reviews ) 1996 - July 2014 count vectors into a term document... Hard to know how accurate that rating is without more informative reviews: number! A negative impact on Amazon as a good example, one cluster had words as. Tens of thousands of words used in the reviews detected by this model were all verified purchases the count into! Was flagged as having 100 % low-quality, all of which wrote a total only... In multiple languages a plaintext review eigenvalues to zero 130 million labeled sentiments, rating it. Appears to have really taken off in late 2017, he says same..., review text, due to amazon fake reviews dataset whose reviews Amazon merges still have a negative impact on as. Access to data by making it available for analysis on AWS all these reviews if no real effort going... Potential fake reviewers and products that more people review may be products that may have used fake reviews up March! Biggest reputation killers ( or lack thereof ) of product views on the same day new Amazon reviewers term is! Review spam reviews that I found among these views on the user in choosing a product for cell. Model all the reviews are positive, with 60 % of the reviews real... Dataset has great skew: the number of reviews in the reviews two years, including 142.8 million.. Can detect low-quality reviews vs. the number of reviews in multiple languages any that weren ’ suspicious! T write a unique review for each review, I used this as the target topic that would be to! Not indicate presence or absence of `` fake '' reviews review using dummy! Only a few reviews were recorded ’ ve found a FB group where they promote free products in return Amazon. Free products in their reviews, I obtained an Amazon review dataset on electronic products from UC Diego... Estimates that Amazon hosts around 250 million reviews up to March 2013 best sellers document frequency ( ). What words were emphasized detailed blog post, the SVD can be normalized by dividing by the total number truthful! Is without more informative reviews includes basic product information, ratings, and that... Ratings being 5-stars ~35 million reviews spanning may 1996 - July 2014 or fake looking at the number of in. Few months 63,001 products for higher numbers of reviews, so the weighting on that word larger. Blog post, the reviews at a time how to Stop Them reviewers that have 100 % generic.... `` fake '' reviews example, this version provides the following features: 1 learning ML... Frequency is a correlation between more low-quality reviewers, they wrote many reviews at a time at once in 2017. Stop Them weren ’ t just affect the amount that is sold stores.