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کلاس‌بندی پیام‌های منتشر شده در شبکههای اجتماعی در بحران کرونا بر‌اساس قطبیت آن‌ها | ||
| مدل سازی در مهندسی | ||
| دوره 21، شماره 74، آبان 1402، صفحه 81-93 اصل مقاله (840.34 K) | ||
| نوع مقاله: مقاله کامپیوتر | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2023.29412.2384 | ||
| نویسندگان | ||
| هانیه تندرست1؛ سمیرا عباسی* 1؛ فاطمه امیری2 | ||
| 1گروه مهندسی پزشکی، دانشگاه صنعتی همدان، همدان، ایران. | ||
| 2گروه مهندسی کامپیوتر، دانشگاه صنعتی همدان، همدان، ایران. | ||
| تاریخ دریافت: 09 دی 1401، تاریخ بازنگری: 19 فروردین 1402، تاریخ پذیرش: 06 اردیبهشت 1402 | ||
| چکیده | ||
| بحران کرونا مردم ایران را با طیف گسترده‌ای از افکار و احساسات مثبت و منفی روبرو کرد. مردم این احساسات را در شبکههای اجتماعی به اشتراک می‌گذاشتند. شبکههای اجتماعی در دوران کرونا نقش بسیار مهمی در انتشار اطلاعات و بازتاب احساسات مردم داشته‌اند. بررسی این داده‌های شبکههای اجتماعی برای دولت‌ها و سازمان‌های بهداشت در سراسر جهان حائز اهمیت است. به همین خاطر پژوهش‌های زیادی به بررسی این داده‌ها با رویکردهای مختلف در سراسر جهان پرداختند. در این مقاله نیز به تحلیل قطبیت و کلاس‌بندی پیام‌های منتشر شده در شبکههای اجتماعی در بحران کرونا پرداخته شد. برای این منظور پیام‌هایی که کاربران فارسی زبان در این شبکه‌ها به اشتراک گذاشتند، بررسی شدند. برای کلاس‌بندی داده‌های موجود از روش‌های پردازش زبان طبیعی و روش‌های یادگیری عمیق استفاده شد. برای کلاس‌بندی پیام‌ها با محتوای مثبت و منفی، روش‌های یادگیری عمیق مختلفی با معماری های متفاوت (شامل شبکه های کانولوشنی، شبکه های بازگشتی عمیق با حافظه و فازی-بازگشتی عمیق با حافظه) با توجه به داده‌های موجود اعمال شد تا بتوانیم به بهترین نتیجه ممکن دست یابیم. بهترین نتیجه با استفاده از شبکههای عمیق کانولوشنی سه‌لایه به دست آمد که صحت آن 72.29 بود. در نهایت، یک مقایسه کلی از جنبه‌های مختلف، روی شبکههای استفاده شده انجام شد. | ||
| کلیدواژه‌ها | ||
| شبکههای اجتماعی؛ ویروس کرونا؛ پردازش زبان طبیعی؛ یادگیری عمیق؛ قطبیت | ||
| عنوان مقاله [English] | ||
| Classification of social media posts during the Corona crisis based on their polarity | ||
| نویسندگان [English] | ||
| Hanie Tandorost1؛ Samira Abbasi1؛ Fatemeh Amiri2 | ||
| 1Biomedical engineering department, Hamedan university of technology, Hamedan, Iran. | ||
| 2ِComputer engineering department, Hamedan university of technology, Hamedan,, Iran. | ||
| چکیده [English] | ||
| The Iranian people were confronted with a range of emotions during the Covid-19 crisis, which they shared on social media platforms. Social media played a crucial role in disseminating information and reflecting public sentiment during the pandemic. Consequently, governments and health organizations worldwide recognized the importance of analyzing social media data. Many researchers have examined these data using different approaches worldwide. This study focuses on the polarity analysis and classification of messages posted on social media during the COVID-19 crisis. The study analyzed messages shared by Persian-language users on social networks using natural language processing and deep learning techniques. Various deep learning methods, including convolutional neural networks (CNN), long short-term memory (LSTM), and fuzzy-LSTM were used to classify the data as positive or negative polarity. The three-layer deep convolutional neural network achieved the highest accuracy of 72.29%. Finally, a comprehensive comparison of the different networks used was conducted across multiple aspects. | ||
| کلیدواژه‌ها [English] | ||
| Social media, Corona virus, Natural language processing (NLP), Deep learning, Polarity | ||
| مراجع | ||
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