Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/148703
Title: Detección de discurso de odio contra la comunidad LGTBI+ mexicana mediante el uso de arquitecturas transformer
Author: Fernández Rosauro, Carlos
Cuadros Oller, Montse
Abstract: The goal of this thesis is to contribute to the improvement of equality for people from the LGTBI+ collective through the detection of LGTB-phobic content in the context of social networks. The technical side of the project is defined by two subtasks organized under the name of HOMO-MEX by the IberLEF 2023 conference. The first subtask consists of a multiclass classification problem to detect LGTBI-phobic tweets, while the second subtask consists of detecting the type specific LGTB-phobia exhibited by the LGTB-phobic tweets through a multilabel classification problem. The two subtasks have been tackled as text classification problems with both basic and advanced Natural Language Processing techniques. More specifically, baseline models, as well as Transformer models based on BERT and similar architectures have been used for both subtasks. The Transformer models have obtained excellent results in the validation phase both in the experimental environment and in the classification leaderboard of the task, as the RoBERTuito model obtained the second position in both subtasks. The Linear SVC baseline model of the traditional type and with lower computational cost obtained very similar results to those of the Transformer models in the experimental context, giving space to the use of simpler models in text classification tasks applied to social networks.
Keywords: NLP
text Classification
hate speech
Gender studies
Type: info:eu-repo/semantics/bachelorThesis
Issue Date: 18-Jun-2023
Publication license: http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Appears in Collections:Bachelor thesis, research projects, etc.

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