Sexism comprises any form of oppression or prejudice against women because of their sex. The aim of the EXIST dataset is to cover sexism in a broad sense, from explicit misogyny to other subtle expressions that involve implicit sexist behaviours.
The MeTwo dataset is a corpus for the detection of sexist expressions and attitudes in Twitter. MeTwo is the first corpus in Spanish designed to identify sexism in a broad sense, from hostile to much more subtle sexism.
The RepLab summarization dataset contains companies data from the RepLab 2013 dataset. The collection comprises tweets about 31 entities from two domains: automotive and banking. As a result, our subset of RepLab 2013 comprises 71,303 English and Spanish tweets.
The eDiseases dataset contains patient data from the MedHelp. We extracted 146 posts for allergies, 191 posts for crohn, and 142 posts for breast cancer; which include 983 sentences for allergies, 1780 sentences for crohn, and 1029 sentences for breast cancer. Each sentence in the dataset is labeled with Factuality (OPINION, FACT, EXPERIENCE) and Polarity (POSITIVE, NEUTRAL, NEGATIVE).
RepLab 2014 focuses on Reputation Monitoring on Twitter, targeting two new tasks: the categorization of messages with respect to standard reputation dimensions (Performance, Leadership, Innovation, etc.) and the characterization of Twitter profiles (author profiling) with respect to a certain activity domain.
The RepLab 2013 task is a (multilingual) evaluation exercise for Online Reputation Management systems. RepLab 2013 focused on monitoring the reputation of entities (companies, organizations, etc.) on Twitter. The monitoring task consists of filtering those that do refer to the entity, detecting topics (i.e., clustering tweets by subject) and ranking them based on the degree to which they signal reputation alerts.
The HotelReview Corpus is a corpus of 1000 reviews extracted from booking.com where each review has been manually tagged with a 5-classes category within the set Excellent, Good, Fair, Poor, Very poor and with a 3-classes category within the set Good, Fair, Poor.