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Amazon review dataset
https://github.com/mlepage/HackReduce
(Hack/Reduce Ottawa) Check out what the team did here: http://www.hackreduce.org/2011/08/shopping-and-dating-hacks-ottawa/
Team-RIM was voted as winner by the participants. For the amazon review data, team-RIM calculated the average rating given on specific dates calculated over all of the years in the dataset. You can see that all products are almost given a rating of 4. You can also notice that right after christmas the ratings drop off, ie. people give worse review right after christmas.
The amazon dataset also includes data over how useful reviews are. The reviews can be voted up or down on amazon by users. Team-RIM analyzed this data and came to the conclusion that reviews that give a higher rating to a product are considered more useful. The team also analyzed the usefulness of reviews based on the review length. From the picture we can see that 50-60 character reviews are considered most useful. As a bonus, the team calculated that products received worse reviews as time went by.
It's "Team-RIM".
Team UOttawaNLP analyzed the sentiment of Amazon product reviews. The trends between the percentage of particular sentiment words and a review's rating of a product. The Java code is very rough, but the data found in the /runs/ folders may be of interest, and scripts to convert it into Weka's ARFF format are provided.