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For each review, we have the text, the author (the reviewer to be precise), the product it was written for, the time stamp of posting, and evaluation.
Two reviewers will independently perform data extraction for each review and populate a predefined table.
Title, abstract, and full text screening was undertaken by four reviewers, and inter-rater reliability statistics were calculated for each review phase.
And each review is heavily illustrated with high-resolution photographs, more than 100 for each review, enough to offer a full view of that lavish lobby as well as the questionable state of the shower stall caulking.
Crucially, the writer needs to be able to create a unique name, email and internet provider address for each review, and make it look like it is posted in the UK, to fool controls on review sites.
Also, the best results for each review dataset are given in bold-face.
For each review we donate one household water filter that removes microorganisms, protozoa, bacteria and cysts from contaminated water.
As observed in Table 2, our new method QER is the best performer for each review dataset.
Although what appears on the platform is anonymous, Comparably requires email verification for each review to ensure that real people — not spammers — are leaving the reviews.
Neighborhood ratings don't strike me as something a broad audience is particularly passionate about and rely on only a small pool of contributors for each review.
All of these settings are run against four classifiers: NBM, SVM, LR, and J48, resulting in a total of 120 experiments for each review dataset.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com