Thu, Nov 7, 2024
[Archive]
Volume 16, Issue 3 (7-2024)
itrc 2024, 16(3): 9-18 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Sobhi M, Mazochi A, Zeinali H. A Comparative Study of BERT-X for Sentiment Analysis and Stance Detection in Persian Social Media. itrc 2024; 16 (3) :9-18
URL: http://journal.itrc.ac.ir/article-1-649-en.html
URL: http://journal.itrc.ac.ir/article-1-649-en.html
1- Department of Computer Engineering Amirkabir University of Technology Tehran, Iran
2- Department of Computer Engineering Amirkabir University of Technology Tehran, Iran ,hzeinali@aut.ac.ir
2- Department of Computer Engineering Amirkabir University of Technology Tehran, Iran ,
Abstract: (109 Views)
BERT-based models have gained popularity for addressing various NLP tasks, yet the optimal utilization of knowledge embedded in distinct layers of BERT remains an open question. In this paper, we introduce and compare diverse architectures that integrate the hidden layers of BERT for text classification tasks, with a specific focus on Persian social media. We conduct sentiment analysis and stance detection on Persian tweet datasets. This work represents the first investigation into the impact of various neural network architectures on combinations of BERT hidden layers for Persian text classification. The experimental results demonstrate that our proposed approaches can outperform the vanilla BERT that utilizes an MLP classifier on top of the corresponding output of the CLS token in terms of performance and generalization.
Keywords: BERT, Persian Text Classification, Social Media, Sentiment Analysis, Stance Detection, CNN, LSTM
Type of Study: Research |
Subject:
Information Technology
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
