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مقالات فارسیISIکنفرانسهاژورنالها

A Proposed Model for Persian Stance Detection on Social Media

محل انتشار: ماهنامه بین المللی مهندسی، دوره: 36، شماره: 6
سال انتشار: 1402
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 145

فایل این مقاله در 12 صفحه با فرمت PDF قابل دریافت می باشد

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:
https://civilica.com/doc/1664673

شناسه ملی سند علمی:

JR_IJE-36-6_003

تاریخ نمایه سازی: 6 خرداد 1402

چکیده مقاله:

Stance detection is a recent research topic that has become an emerging paradigm  of the importance of opinion-mining. It is intended to determine the author’s views toward a specific topic or claim. Stance detection has become an important module in numerous applications such as fake news detection, argument search, claim validation, and author profiling. Despite considerable progress made in this regard in languages like English, unfortunately, we have not made good progress in some languages such as Persian, where we are confronted with a lack of datasets in this area. In this paper, two solutions are used to address this issue: ۱) the use of data augmentation and ۲) the application of different learning approaches (machine learning, deep learning, and transfer learning) and a meaningful combination of their outcomes. The results show that each of these solutions can not only enhance stance detection performance, but when both are combined, a very significant improvement in the results is achieved.

کلیدواژه ها:

stance detection ، Data Augmentation ، Fake News ، Machine Learning ، Deep Learning ، Transfer learning ، multi-classifier fusion

نویسندگان

M. Farhoodi

Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

A. Toloie Eshlaghy

Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

M. R. Motadel

Central Tehran Branch, Islamic Azad University Tehran, Iran

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