TY - JOUR
T1 - Irony detection using emotion cues
AU - Calvo, Hiram
AU - Gambino, Omar J.
AU - Mendoza, Consuelo Varinia García
N1 - Publisher Copyright:
© 2020 Instituto Politecnico Nacional. All rights reserved.
PY - 2020
Y1 - 2020
N2 - This work is centered on the data made available for the IroSvA challenge, consisting of three variants of Spanish language from three different countries. We propose a simple model for identifying irony, based on tweet embeddings, refraining from using of additional NLP techniques. We aim to find cues that are able to generalize the knowledge obtained from a language variant, and evaluate the ability to detect irony in different combinations of variants, from different countries and topics. For this purpose, we propose using six features based on the degree of emotion present in each tweet. These automatically tagged features include 5 levels of strength, ranging from none to very high, of six emotions: Love, joy, surprise, sadness, anger, and fear. Experiments were carried out with different combinations of language variants. Obtained results show that exclusively using the information of the emotion levels (discarding the embeddings) could improve the irony detection in a language variant different from that used for training.
AB - This work is centered on the data made available for the IroSvA challenge, consisting of three variants of Spanish language from three different countries. We propose a simple model for identifying irony, based on tweet embeddings, refraining from using of additional NLP techniques. We aim to find cues that are able to generalize the knowledge obtained from a language variant, and evaluate the ability to detect irony in different combinations of variants, from different countries and topics. For this purpose, we propose using six features based on the degree of emotion present in each tweet. These automatically tagged features include 5 levels of strength, ranging from none to very high, of six emotions: Love, joy, surprise, sadness, anger, and fear. Experiments were carried out with different combinations of language variants. Obtained results show that exclusively using the information of the emotion levels (discarding the embeddings) could improve the irony detection in a language variant different from that used for training.
KW - Clues
KW - Emotions
KW - Irony detection
KW - Texts
UR - http://www.scopus.com/inward/record.url?scp=85095688762&partnerID=8YFLogxK
U2 - 10.13053/CYS-24-3-3487
DO - 10.13053/CYS-24-3-3487
M3 - Artículo
AN - SCOPUS:85095688762
SN - 1405-5546
VL - 24
SP - 1281
EP - 1287
JO - Computacion y Sistemas
JF - Computacion y Sistemas
IS - 3
ER -