Ammar Mars
2024
ISHFMG_TUN at StanceEval: Ensemble Method for Arabic Stance Evaluation System
Ammar Mars
|
Mustapha Jaballah
|
Dhaou Ghoul
Proceedings of The Second Arabic Natural Language Processing Conference
It is essential to understand the attitude of individuals towards specific topics in Arabic language for tasks like sentiment analysis, opinion mining, and social media monitoring. However, the diversity of the linguistic characteristics of the Arabic language presents several challenges to accurately evaluate the stance. In this study, we suggest ensemble approach to tackle these challenges. Our method combines different classifiers using the voting method. Through multiple experiments, we prove the effectiveness of our method achieving significant F1-score value equal to 0.7027. Our findings contribute to promoting NLP and offer treasured enlightenment for applications like sentiment analysis, opinion mining, and social media monitoring.
Search