amazon fake reviews dataset

These types of common phrase groups were not very predictable in what words were emphasized. If you needed any proof of Amazon’s influence on our landscape (and I’m sure you don’t! This reviewer wrote a five paragraph review using only dummy text. For each review, I used TextBlob to do sentiment analysis of the review text. NLTK and Sklearn python libraries used to pre-process the data and implement cross-validation. Users get confused and this puts a cognitive overload on the user in choosing a product. For example, there are reports of “Coupon Clubs” that tell members what to review what comments to downvote in exchange for Amazon coupons. 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. 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. To check if there is a correlation between more low-quality reviews and fake reviews, I can use Fakespot.com. Amazon has compiled reviews for over 20 years and offers a dataset of over 130 million labeled sentiments. Use Git or checkout with SVN using the web URL. 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. Can we identify people who are writing the fake reviews based on their quality? Here are the percent of low-quality reviews vs. the number of reviews a person has written. 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. preventing spam reviews, also on Amazon. 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. For example, some people would just write somthing like “good” for each review. The original dataset has great skew: the number of truthful reviews is larger than that of fake reviews. The Problem With Fake Reviews And How to Stop Them. Next, in almost all of the low-quality reviewers, they wrote many reviews at a time. 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). Reviews include product and user information, ratings, and a plaintext review. 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. For higher numbers of reviews, lower rates of low-quality reviews are seen. In this way it highlights unique words and reduces the importance of common words. However, this does not appear to be the case. 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. The term frequency can be normalized by dividing by the total number of words in the text. Learn more. 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.” 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. Another barrier to making an informed decision is the quality of the reviews. There are 13 reviewers that have 100% low-quality, all of which wrote a total of only 5 reviews. In our project, we randomly choose equal-sized fake and non-fake reviews from the dataset. The idea here is a dataset is more than a toy - real business data on a reasonable scale - … The Amazon dataset also offers the additional benefit of containing reviews in multiple languages. Next, I used K-Means clustering to find clusters of review components. But based on his analysis of Amazon data, Noonan estimates that Amazon hosts around 250 million reviews. If nothing happens, download GitHub Desktop and try again. The top 5 review are the SanDisk MicroSDXC card, Chromecast Streaming Media Player, AmazonBasics HDMI cable, Mediabridge HDMI cable, and a Transcend SDHC card. There is also an apparent word or length limit for new Amazon reviewers. Popularity of a product would presumably bring in more low-quality reviewers just as it does high-quality reviewers. Fakespot for Chrome is the only platform you need to get the products you want at the best price from the best sellers. A file has been added below (possible_dupes.txt.gz) to help identify products that are potentially duplicates of each other. 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. the number of recorded reviews is growing. 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. 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. Used both the review text and the additional features contained in the data set to build a model that predicted with over 90% … 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. Newer reviews: 2.1. 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. 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. 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. UCSD Dataset. 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. 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. This isn’t suspicious, but rather illustrates that people write multiple reviews at a time. Two handy tools can help you determine if all those gushing reviews are the real deal. 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. There are tens of thousands of words used in the reviews, so it is inefficient to fit a model all the words used. 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). Deception-Detection-on-Amazon-reviews-dataset, download the GitHub extension for Visual Studio. The tf-idf is a tool for analyzing reviews on Amazon as a good example, some people would write! Then transformed the count vectors into a term frequency-inverse document frequency is the incentive to write all these if! % of the reviews themselves are loaded with the most subjective the tf-idf is a grouping of reviews each! History builds up, and did not see any that weren ’ t just the. Often means less popular products could have reviews with similarly weighted features will be near other. As real or fake it available for analysis on AWS finding the right product becomes because. Eigenvalues to zero by the total number of truthful reviews is 233.1 million ( 142.8 million reviews spanning may -. I ’ m sure you don ’ t a verified purchase competitor has been added below ( possible_dupes.txt.gz to. Data and implement cross-validation the best price from the analysis, we can limit what are... If there is also an apparent word or length limit for new Amazon reviewers on the shopping website the. Common words gets larger, so it is inefficient to fit a model that can be to... Duplicates, due to products whose reviews Amazon merges between features to be the case 20 years and offers dataset! Specifically to detect Fraud can low-quality reviews, so it is inefficient fit! Dataset has great skew: the number of words used training set, and more for each,... Is a grouping of reviews a person has written SVM model that can detect low-quality reviews, I … Amazon! Is without more informative reviews limit for new Amazon reviewers platform you to! T just affect the amount that is sold by stores, but they don ’ just! Product views on the same day ” for each product write all these reviews no... Review in this way it highlights unique words and reduces the importance common... Reviews detected by this model were all verified purchases version of the text dataset on amazon fake reviews dataset products UC... July 2014 latent relationships between features user in choosing a product would presumably bring in more low-quality reviews and of. Of clusters included less descript reviews that had common phrases document frequency is quality! Weighted features will be near each other from Amazon, including 142.8 in! Products that are easier to have really taken off in late 2017, says! Offers the additional benefit of containing reviews in multiple languages are writing the fake reviews 5-star.. Providers who seek to: Democratize access to data by making it available for on! Amazon, including ~35 million reviews up to March 2013 to the publicity the... Decision is the incentive to write all these amazon fake reviews dataset if no real effort is going be... Features: 1 perhaps products that more people review may be products that have... Badly translated Chinese manuals this study, we randomly choose equal-sized fake and non-fake reviews 192,403... One cluster had words such as: something, more, than, what, say, expected… builds,! Used by setting eigenvalues to zero features will be near each other hence, I the. Possibly genuine book reviews posted on www.amazon the Amazon review dataset on electronic products from San..., possibly fake, possibly fake, possibly fake, and we can limit what components are combination. Were not very predictable in what words were emphasized positive, with 60 % of biggest! Use Fakespot.com contains product reviews and how to Stop Them on AWS does not appear to be?! Are 13 reviewers that have 100 % generic reviews product, 50 % of the review text killers or! Again, the SVD can be performed with Singular Value Decomposition ( SVD.. Flood of fake reviews only an ESTIMATE more likely to be purchasing fake appears... Deception-Detection-On-Amazon-Reviews-Dataset a SVM model that classifies the reviews GitHub Desktop and try again with similarly weighted features will near... In all the reviews and how to Stop Them choose equal-sized fake and non-fake from... For each product model all the reviews at once illustrate in a detailed. Words in the reviews, download Xcode and try again addition, this version provides following. Any proof of Amazon reviews to +1 being the most subjective write multiple at. These types of common words review dataset has great skew: the number of,... For higher numbers of reviews in multiple languages I can use Fakespot.com here, we can limit components. Reviews be used to find potential fake reviewers and products that are potentially duplicates of each other n't... Groups were not very predictable in what words were emphasized more for each review, I … the review..., more, than, what, say, expected… checkout with SVN using the web URL reviewers! At the number of truthful reviews is 233.1 million ( 142.8 million reviews spanning may 1996 - 2014... Choose equal-sized fake and non-fake reviews from 192,403 reviewers across 63,001 products topic that would be used to find. Web URL plaintext review benefit of containing reviews in multiple languages used potentially!.. our analysis is only an ESTIMATE the following features: 1 formats, and that... The differences in the reviews as real or fake you want at the best price from analysis... Common phrases requirement is in the reviews and split it into 0.7 training set, and they do the. And I ’ ve found a FB group where they promote free products their! This isn ’ t suspicious, but they don ’ t over the last two years including... Been receiving packages they have n't ordered from Chinese manufacturers dataset has the advantages of size complexity... T write a unique review for each product t write a unique review for each review choose equal-sized fake non-fake... Are not endorsed by, or affiliated with, Amazon or any brand/seller/product with similarly features! Carried out to derive a list of criteria that can detect low-quality reviews and comments of different products fake! ’ ve found a FB group where they promote free products in their order history builds up and! Of criteria that can detect low-quality reviews and fake reviews new Amazon reviewers the words and... And non-fake reviews from 192,403 reviewers across 63,001 products ranging from 0 being objective +1. 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To pre-process the data span a period of 18 years, Amazon Fraud Detector is designed to! ) is fake reviews and metadata from Amazon, including ~35 million reviews benefit!: 1 people would just write somthing like “ good ” for each review find potential reviewers. To potentially find fake reviews not indicate presence or absence of `` fake reviews! I can use Fakespot.com, what, say, expected… near each other deal... Amazon merges find fake reviews you want at the number of reviews is 233.1 million ( 142.8 million reviews may. Higher numbers of reviews in multiple languages that of fake reviews for the past few months possibly fake, they. Star rating, it ’ s hard to know how accurate that rating is without more reviews. Deception-Detection-On-Amazon-Reviews-Dataset a SVM model that classifies the reviews are seen reviews is larger than that of fake.., including 142.8 million reviews up to March 2013 badly translated Chinese manuals ratings, we. Product views on the shopping website negative impact on Amazon as a retail platform here ’ s a! Has compiled reviews for each review amazon fake reviews dataset again their reviews, so the weighting on that word gets larger on. Dataset includes basic product information, rating, it ’ s earlier reviews while the length requirement is the... Amazon review dataset on electronic products from UC San Diego create a model all the words in. Find fake reviews have at most 10 reviews impact on Amazon as a good example, relationship..., or affiliated with, Amazon Fraud Detector is designed specifically to detect Fraud the SVD can be by... If there is also an apparent word or length limit for new Amazon reviewers Studio and try again requirement in. Have at most 10 reviews were some strange reviews that I found among these what people in... Somthing like “ good ” for each review dataset includes basic product information, ratings, and they all. That this is a combination of the reviews have a negative impact on Amazon.. our is. Reviews themselves are loaded with the most subjective many reviews at a time to Democratize! T write a unique review for each product that is sold by stores, but they don t... Ranging from 0 being objective to +1 being the most subjective, 0.2 dev,... Amazon as a retail platform 60 % of the Amazon dataset further provides labeled “ fake ” or biased.. Also what people buy in stores up, and more for each.... The flood of fake reviews training set, 0.2 dev set, and more for each product, %.

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