Open AccessHumor Detection using Support Vector Machine
Author(s) -
Marina Pinho Garcia,
Giovana Pinho Garcia,
Nádia Félix Felipe da Silva
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/erigo.2021.18437
Subject(s) - support vector machine , computer science , artificial intelligence , classifier (uml) , machine learning , f1 score , field (mathematics) , pattern recognition (psychology) , natural language processing , data mining , mathematics , pure mathematics
This paper aims classify texts in humorous and non-humorous, while exploring the different parameters and tactics that can be used alongside the Support Vector Machine (SVM) classifier, to see and understand their impact on the classification and find the best combinations that have the best performances considering the accuracy and the F1 score. After observing the plots and analyzing the data we were able to come to a conclusion of which combination would be best to classify the texts in the testing data provided by the HaHackathon: Detecting and Rating Humor and Offense CodaLab Competition [cod 2021]. With those results we were able to give a wide view of this type of problem solutions, which can be used in further related work in this field of research.