تجزیه و تحلیل احساسات دو وجهی حساس به میم مبتنی بر تبدیل کننده با استفاده از داده های بصری-متن در رسانه های اجتماعی
محورهای موضوعی : شبکه های عصبی و یادگیری عمیقزهرا پاکدامن 1 , عباس کوچاری 2 , آرش شریفی 3
1 - دانشگاه آزاد اسلامی
2 - Department of Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran
3 - Department of Computer Engineering, Mechanics, electricity and computers, Islamic Azad University - Science and Research Branch, Tehran, Iran
کلید واژه: تحلیل احساس بصری, تحلیل احساس متنی, انتقال‌دهنده بصری, آنالیز تمایزکننده خطی, SBERT,
چکیده مقاله :
تحلیل احساس داده‌های شبکه‌های اجتماعی به درک هدف اصلی کاربران از انتشار محتوا کمک می‌کند. از آنجایی که داده‌های انتشاریافته تنوع زیادی دارند (تصویر، متن، صدا و ویدئو)، از این تنوع می‌توان برای دستیابی به تحلیل احساس دقیق‌تر استفاده کرد. در مقاله حاضر یک معماری دووجهی حساس به میم پیشنهاد شده است که از داده‌های متنی و بصری برای تحلیل احساس استفاده می‌کند. در این روش پس از استخراج متن درج شده در میم و الحاق آن با توضیحات انتشاریافته توسط کاربر، ویژگی‌های متن و تصویر با استفاده از انتقال‌دهنده استخراج می‌شود. برای استخراج ویژگی‌های تصویر از انتقال‌دهنده بصری و برای استخراج ویژگی‌های متن از SBERT استفاده شده است. سپس تبدیل تحلیل تشخیص خطی (LDA) برای کاهش ابعاد و افزایش کیفیت دسته‌بندی به ویژگی‌های استخراج شده اعمال می‌شود. در نهایت دو لایه کاملا متصل، بردار حاصل از کاهش بعد را برای پیش‌بینی کلاس احساس پردازش می‌کنند. نتایج تجربی نشان می‌دهد که روش پیشنهادی بر روی مجموعه داده MVSA-Single به دقت ۸۰ درصد و بر روی مجموعه داده MVSA-Multiple به دقت ۹۰ درصد دست یافته است. این نتایج بیان می‌کند که روش ارائه شده در مقاله نسبت به سایر روش‌های مطرح تحلیل احساس دووجهی عملکرد بهتری دارد.
Analyzing the sentiment of the social media data helps to understand the main purpose of users. Since the published data has a large diversity (i.e., image, text, audio, and video), this variety can be used to achieve a more accurate sentiment analysis. This study introduces a meme-sensitive cross-modal architecture for analyzing users’ feelings using both visual and textual data. The research process involves the recognition of memes from regular images. Following the extraction of embedded text in memes and its concatenation with the user’s caption, text and image features are extracted using transformers. Specifically, Vision Transformer (ViT) and SBERT Bi-encoder are used for visual and textual feature extraction, respectively. Subsequently, Linear Discriminant Analysis (LDA) transformation is applied to reduce dimensionality and enhance classification. Finally, two fully connected layers process the resulting vector to predict the sentiment class. Experimental results show an achievement of up to 90% on MVSA-Single and 80% on MVSA-Multiple datasets for the proposed method. Therefore, it has superior performance compared to other state-of-the-art ones.
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