A dataset containing a random sample of 50,000 reviews of fine foods from Amazon between Oct. 2011 - Oct. 2012. These data include the fraction of users who found the review helpful (numerator and denominator). We selected one year from the original dateset of 568,454 taken from 1999-2012, and then randomly sample 50,000 from that subset.
data(corpus_finefoods)A data frame with 50000 rows and 9 variables.
http://snap.stanford.edu/data/web-FineFoods.html
review_id. Unique ID for the review
product_id. Unique ID for the product being reviewed
user_id. Unique ID for the reviewer
profile_name. Name of the reviewer
helpfulness_numerator. Number of users who find the review helpful
helpfulness_denominator. Total number of users rating helpfulness
score. Rating of the produce by the reviewer
summary. Review summary
text. Text of the review
datetime. Time of the review (in datatime UTC)
J. McAuley and J. Leskovec. (2013) From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews. WWW