Publication

Abstract : This paper discusses deepyCybErNet submission methodology to the task on Stance and Gender Detection in Tweets on Catalan Independence@Ibereval 2017. The goal of the task is to detect the stance and gender of the user in tweets on the subject ”independence of Catalonia”. Tweets are available in two languages: Spanish and Catalan. In task 1 and 2, the system has to determine whether the tweet is in favor of, against or neutral to the tweets on the subject pertaining to the task in Spanish and Catalan languages respectively. In task 3 and 4, the system has to decide whether the person who tweets is a male or female. We submitted three systems for this task a Bag-of-Words (BOW) representation for tweets with logistic regression classifier, Recurrent Neural Network (RNN) based approach, Long Short Term Memory (LSTM) based approach and gated recurrent based approach. These methods are highly language independent and can be used for the declarations of stance of tweets and identifying the gender of twitter user in any language. These methods have performed better in detecting stance and gender in tweets of Catalan language than in those of Spanish.