TY - JOUR
T1 - THANGCIC at PoliticEs 2022
T2 - 2022 Iberian Languages Evaluation Forum, IberLEF 2022
AU - Ta, Hoang Thang
AU - Rahman, Abu Bakar Siddiqur
AU - Najjar, Lotfollah
AU - Gelbukh, Alexander
N1 - Publisher Copyright:
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
PY - 2022
Y1 - 2022
N2 - This paper presents our participation in the task of detecting gender, profession, and political ideology in tweets of Spanish users, in a binary and multi-class perspective. The task plays an important role in identifying political ideology of parties and politicians, especially new emerging ones. This may support relevant tasks to make predictions in the elections, or create an impact on the decision of citizens through out propagation systems. For each user, we extracted features as the most popular terms from a bunch of his/her tweets, then put them as input data for the training, which applied a transfer learning set up on pre-trained BERT models. Our quick method should be suggested as a baseline for the task with the highest F1 average macro of 72.72%. In detail, we obtained F1 Gender of 69.14%, F1 Profession of 81.47%, F1 Ideology Binary of 75.76%, and F1 Ideology Multiclass of 64.51%.
AB - This paper presents our participation in the task of detecting gender, profession, and political ideology in tweets of Spanish users, in a binary and multi-class perspective. The task plays an important role in identifying political ideology of parties and politicians, especially new emerging ones. This may support relevant tasks to make predictions in the elections, or create an impact on the decision of citizens through out propagation systems. For each user, we extracted features as the most popular terms from a bunch of his/her tweets, then put them as input data for the training, which applied a transfer learning set up on pre-trained BERT models. Our quick method should be suggested as a baseline for the task with the highest F1 average macro of 72.72%. In detail, we obtained F1 Gender of 69.14%, F1 Profession of 81.47%, F1 Ideology Binary of 75.76%, and F1 Ideology Multiclass of 64.51%.
KW - Author Profiling
KW - BERT
KW - IberLEF
KW - Political Ideology
KW - SEPLN
KW - Text Classification
UR - http://www.scopus.com/inward/record.url?scp=85137336482&partnerID=8YFLogxK
M3 - Artículo de la conferencia
AN - SCOPUS:85137336482
SN - 1613-0073
VL - 3202
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 20 September 2022
ER -