Opinion Mining in Twitter

The dataset consists in 3036 tweets expressing opinions about movies in theatres. Each tweet is manually classified into four categories, according to the type of opinion expressed: negative (755 instances), neutral (615 instances), positive (1040 instances), mixed (626 instances). The "neutral" category represents tweets in which a movie is mentioned, but no sentiment is expressed about it. The "mixed" category represent tweets expressing both a positive and a negative opinion, regarding different aspects of the movie. 

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Processes logs Dataset

The dataset consists of eight process logs artificially generated using PLG. The dataset is divided in four folders. The first folder corresponds to sequences generated from the execution of the process workflow without noise. The other three folders contain logs generated by introducing 5%, 10% and 20% of noise in the normal sequences belonging to the workflow process.

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User Engagement Dataset

The dataset consists of 153 answers collected since December 2013 about the experience in the use of a Movie Recommender system available online at

The online questionary consisted of 35 questions regarding the perceived ease of use, perceived usefulness, believed skills and previous skills in the use of recommender systems, complexity of the user interface and engagement with the system under evaluation.

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