We describe the system submitted to IBEREVAL-2017 for stance and gender detection in tweets on Catalan Independence . We developed a supervised system using Support Vector Machines with ra- dial basis function kernel to identify the stance and gender of the tweeter using various character level and word level features. Our system achieves a macro-average of F-score(FAVOR) and F-score(AGAINST) of 0.46 for stance detection in both Spanish and Catalan and an accuracy of 64.85% and 44.59% for Gender detection in Spanish and Catalan respectively.
|Original language||English (US)|
|Number of pages||5|
|Journal||CEUR Workshop Proceedings|
|State||Published - Jan 1 2017|
|Event||2nd Workshop on Evaluation of Human Language Technologies for Iberian Languages, IberEval 2017 - Murcia, Spain|
Duration: Sep 19 2017 → …