تشخیص عددی قطبیت با کاربست شبکه‌های عمیق بازگشتی و یادگیری بانظارت در نظرکاوی بر روی مرورهای فارسی کاربران حوزه تجارت الکترونیک
محورهای موضوعی : مهندسی برق و کامپیوترسپیده جمشیدی نژاد 1 , فاطمه احمدی آبکناری 2 , پیمان بیات 3
1 - دانشگاه آزاد اسلامی واحد رشت
2 - دانشگاه پیام نور رشت
3 - دانشگاه آزاد اسلامی واحد رشت
کلید واژه: پردازش زبان طبیعی, تحلیل احساس, تشخیص قطبیت جملات, تشخیص هرزنظر, شبکه‌های عصبی عمیق, نظرکاوی,
چکیده مقاله :
نظرکاوی، زیرشاخه‌ای از داده‌کاوی است که به حوزه پردازش زبان طبیعی وابسته بوده و با گسترش تجارت الکترونیکی، به یکی از زمینه‌های محبوب در بازیابی اطلاعات تبدیل شده است. این حوزه بر زیرمجموعه‌های مختلفی مانند تشخیص قطبیت، استخراج جنبه و تشخیص هرزنظر تمرکز دارد. اگرچه وابستگی نهانی بین این زیرمجموعه‌ها وجود دارد اما طراحی یک چارچوب جامع شامل تمامی این موارد، بسیار چالش‌برانگیز است. پژوهش‌های موجود در این حوزه اکثراً بر روی زبان انگلیسی بوده و برای تحلیل احساس، بدون توجه به زیرمجموعه‌های تأثیرگذار، فقط بر روی حالت باینری تمرکز داشته‌اند. همچنین استفاده از یادگیری ماشینی برای دسته‌بندی نظرات بسیار رایج است و در سال‌های اخیر، اغلب پژوهش‌ها از یادگیری عمیق با اهداف متفاوت استفاده کرده‌اند. از آنجا که در ادبیات پژوهشی به چارچوبی جامع با تمرکز بر زیرمجموعه‌های تأثیرگذار کمتر پرداخته شده است، از این رو در مقاله حاضر با استفاده از راهکارهای نظرکاوی و پردازش زبان طبیعی، چارچوب جامع مبتنی بر یادگیری عمیق با نام RSAD که پیشتر توسط نویسندگان این مقاله در حوزه نظرکاوی کاربران فارسی زبان توسعه داده شده بود برای تشخیص قطبیت در دو حالت باینری و غیر باینری جملات با تمرکز بر سطح جنبه بهبود داده شده که تمام زیرمجموعه‌های لازم برای تحلیل احساس را پوشش می‌دهد. مقایسه و ارزیابی RSAD با رویکردهای موجود، نشان‌دهنده استحکام آن است.
Opinion mining as a sub domain of data mining is highly dependent on natural language processing filed. Due to the emerging role of e-commerce, opinion mining becomes one of the interesting fields of study in information retrieval scope. This domain focuses on various sub areas such as polarity detection, aspect elicitation and spam opinion detection. Although there is an internal dependency among these sub sets, but designing a thorough framework including all of the mentioned areas is a highly demanding and challenging task. Most of the literatures in this area have been conducted on English language and focused on one orbit with a binary outcome for polarity detection. Although the employment of supervised learning approaches is among the common utilizations in this area, but the application of deep neural networks has been concentrated with various objectives in recent years so far. Since the absence of a trustworthy and a complete framework with special focuses on each impacting sub domains is highly observed in opinion mining, hence this paper concentrates on this matter. So, through the usage of opinion mining and natural language processing approaches on Persian language, the deep neural network-based framework called RSAD that was previously suggested and developed by the authors of this paper is optimized here to include the binary and numeric polarity detection output of sentences on aspect level. Our evaluation on RSAD performance in comparison with other approaches proves its robustness.
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