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  [2] shebuti rayana and leman akoglu. collective opinion spam detection:bridging review networks and metadata. in acm sigkdd international conference on knowledge discovery and data mining, pages 985–994, 2015. i'd like to thank dr. moidin mohiuddin, cto of scoredata, for introducing this method to me. as an experiment, we only used about 20,000 rows to create a text vector because it would take a long time to convert 608k+ reviews to a vector. after using the word2vec model, we turned the reviews into a vector. we first separated this vector into training and test sets, where the training set only contains truthful reviews, and the test set only includes the fake reviews (minority). then we standardized the entire text vector using (x(i)-mean(x(i))/sd(x(i)), where i refers to the row number and x refers to the text vector. after that, we passed the standardized x vector into a deep learning neural network to create an approximation of it; the approximated output is y. next, we compute the error term mse e(i) = mean of (y(i)-x(i))² over all rows, and the standard deviation of all error terms. now we can use the square roots of mse (rmse) and sd for the outlier test. if the rmse/sd (z-score) is greater than 3 or less than -3, then we say it is an outlier, hence spam.

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  [2] shebuti rayana and leman akoglu. collective opinion spam detection:bridging review networks and metadata. in acm sigkdd international conference on knowledge discovery and data mining, pages 985–994, 2015. i'd like to thank dr. moidin mohiuddin, cto of scoredata, for introducing this method to me. as an experiment, we only used about 20,000 rows to create a text vector because it would take a long time to convert 608k+ reviews to a vector. after using the word2vec model, we turned the reviews into a vector. we first separated this vector into training and test sets, where the training set only contains truthful reviews, and the test set only includes the fake reviews (minority). then we standardized the entire text vector using (x(i)-mean(x(i))/sd(x(i)), where i refers to the row number and x refers to the text vector. after that, we passed the standardized x vector into a deep learning neural network to create an approximation of it; the approximated output is y. next, we compute the error term mse e(i) = mean of (y(i)-x(i))² over all rows, and the standard deviation of all error terms. now we can use the square roots of mse (rmse) and sd for the outlier test. if the rmse/sd (z-score) is greater than 3 or less than -3, then we say it is an outlier, hence spam.

  　　

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