اعتقادیابی متون فارسی بر اساس یادگیری عمیق با تفکیک احساس-کلمه
الموضوعات :
حسین علی کرمی
1
,
امیرمسعود بیدگلی
2
,
حمید حاج سیدجوادی
3
1 - دانشکده مهندسی کامپیوتر، دانشگاه آزاد تهران شمال، تهران، ایران.
2 - دانشکده مهندسی کامپیوتر، دانشگاه آزاد تهران شمال، تهران، ایران.
3 - Shahed University
الکلمات المفتاحية: اعتقادکاوی, پردازش زبان طبیعی(NLP), یادگیری عمیق, متن کاوی,
ملخص المقالة :
اعتقادکاوی یا طبقه بندی متون بر اساس احساس و عقیده کاربران در وبسایت ها و رسانه های اجتماعی به مردم، شرکت ها و سازمان ها کمک می‌کند تا بتوانند تصمیم گیری های مهم را انجام دهند. اعتقادکاوی شامل یک سیستم برای تحلیل عقاید و احساسات مردم درباره یک موجودیت مانند محصولات، افراد، سازمان ها با توجه به نظرات، پیام ها و توییت های کاربران در رسانه های اجتماعی می باشد. در این مقاله اعتقادکاوی متون فارسی بر اساس پیام ها، نظرات و توییت های کابران در رسانه اجتماعی و وبسایت های ۴ مجموعه داده با استفاده از دو روش یادگیری عمیق CNN , LSTM با در نظر گرفتن احساس کلمه، در دو قطب مثبت و منفی با بازه ۲- و ۲+ طبقه بندی شده اند. در روش پیشنهادی ابتدا فرآیند پیش‌پردازش داده‌ها بر اساس تبدیل کاراکتر به عدد، حذف لیست واژه های اضافی و تحلیل چند واژه‌ای انجام می‌شود، سپس جهت اعتقادکاوی و طبقه‌بندی متون فارسی با الگوریتم یادگیری ماشین CNN , LSTM با تفکیک احساس کلمه (WSD) استفاده می‌شود تا شدت احساسات را با توجه به کلمات تشخیص دهد . مدل پیشنهادی را CNN_WSD و LSTM_WSD می نامیم. در روش پیشنهادی مجموعه داده های فارسی توییتر برای ارزیابی استفاده شده و سپس با سایر روش های یادگیری ماشین و یادگیری عمیق DNN, CNN, LSTM مقایسه می شود، در پیاده سازی این روش از نرم افزار متلب python استفاده شده است. میزان دقت روش پیشنهادی برای LSTM-WSD و CNN-WSD به ترتیب 95.8 و 94.3 درصد است.
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