یک مدل تشخیص احساسات معنایی مبتنی بر آنتولوژی و آتوماتای یادگیر عمیق سلولی
محورهای موضوعی : تخصصی
هوشنگ صالحی
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رضا قائمی
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مریم خیرآبادی
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1 - گروه مهندسی کامپیوتر، واحد نیشابور، دانشگاه آزاد اسلامی، نیشابور، ایران
2 - گروه مهندسی کامپیوتر، واحد قوچان، دانشگاه آزاد اسلامی، قوچان، ایران
3 - گروه مهندسی کامپیوتر، واحد نیشابور، دانشگاه آزاد اسلامی، نیشابور، ایران
کلید واژه: نظر کاوی, تحلیل احساسات, شبکه عصبی عمیق, آتوماتای سلولی, آنتولوژی,
چکیده مقاله :
امروزه شبکه های اجتماعی و رسانه های ارتباطی نقش به سزایی را در زندگی روزمره کاربران دارند. کاربران در زمینه های مختلف در شبکه های اجتماعی اقدام به گفتگو و تبادل اطلاعات می نمایند. در جملات و کامنت های کاربران احساسات منفی و مثبت در رابطه با اخبار روز، اتفاقات موجود و غیره وجود دارد که تشخیص این احساسات با چالش های زیادی مواجه است. تاکنون روش های مختلفی مانند یادگیری ماشین، رویکردهای آماری، هوش مصنوعی و غیره به منظور تشخیص احساسات مطرح شده است که علی رغم کاربردهای فراوانی که داشته اند؛ اما هنوز نتوانسته دقت، شفافیت و صحت قابل قبولی داشته باشند. بنابراین در این مقاله، یک مدل نظرکاوی معنایی مبتنی بر آنتولوژی با استفاده از آتوماتای یادگیر عمیق سلولی مبتنی بر شبکه عصبی عمیق GMDH ارائه شده است. از رویکرد آنتولوژی برای انتخاب ویژگی های برجسته مبتنی بر قوانین تولید و از آتوماتای یادگیر عمیق سلولی برای طبقه بندی احساسات کاربران استفاده می‌‌شود. نوآوری اصلی این مقاله الگوریتم پیشنهادی آن است که یک روش یادگیری عمیق جهت پردازش تنها یک عبارت توسعه داده شده و سپس با انتقال آن به حوزه آتوماتای سلولی، پردازش موازی و یا توزیع شده آن فراهم می شود. در این مقاله، از مجموعه داده های مشتریان آمازون، توئیتر، فیس بوک، اخبار جعلی COVID-19، آمازون و شبکه اخبار جعلی استفاده شده است. با شبیه سازی روش پیشنهادی مشاهده گردید که روش پیشنهادی نسبت به سایر روش های دیگر به طور میانگین 3% بهبود داشته است
Today, social networks and communication media play a significant role in the daily life of users. Users talk and exchange information in different fields in social networks. In the sentences and comments of users, there are negative and positive feelings in relation to the news of the day, current events, etc., and recognizing these feelings faces many challenges. So far, various methods such as machine learning, statistical approaches, artificial intelligence, etc., have been proposed for the purpose of detecting emotions, which despite their many applications; But they have not yet been able to have acceptable accuracy, transparency and accuracy. Therefore, in this article, an ontology-based semantic analysis model using cellular deep learning automata based on GMDH deep neural network is presented. Ontology approach is used to select salient features based on production rules and cellular deep learning automata is used to classify user sentiments. The main innovation of this article is the proposed algorithm that a deep learning method is developed to process only one expression and then by transferring it to the field of cellular automata, parallel or distributed processing is provided. In this article, the data sets of Amazon customers, Twitter, Facebook, fake news of COVID-19, Amazon and fake news network are used. By simulating the proposed method, it was observed that the proposed method has an average improvement of 3% compared to other methods
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