آنالیز حس اسناد فارسی با طراحی حوزه تبدیل بهینه
محورهای موضوعی : مهندسی برق و کامپیوترآصف پورمعصومی 1 , هادی صدوقی یزدی 2 , هادی قائمی 3 , زهرا دلخسته 4
1 - دانشگاه فردوسی مشهد
2 - دانشگاه فردوسی مشهد
3 - دانشگاه فردوسی مشهد
4 - دانشگاه فردوسی مشهد
کلید واژه: آنالیز حس حوزه تبدیل حداکثرکردن انرژی طیفی,
چکیده مقاله :
با توسعه تعاملات مبتنی بر وب نظیر نظرسنجی‌ها، وبلاگ‌های شخصی و شبکه‌های اجتماعی، آنالیز حس و یا کاوش عقیده به یکی از حوزه‌های تحقیقاتی مهم در علوم کامپیوتر تبدیل شده است. تا کنون روش‌های زیادی مبتنی بر یادگیری ماشین و همچنین پردازش زبان طبیعی در ارتباط با آنالیز حس ارائه شده است. در این مقاله از توزیع کلمات در مجموعه اسناد جمع‌آوری شده به عنوان معیاری جدید برای تشخیص حس جمله استفاده شده است. در روش پیشنهادی با طراحی حوزه تبدیل بهینه مناسب روی توزیع کلمات، دو هدف حداکثرکردن انرژی طیفی کلاس 1 در فرکانس‌های پایین و حداکثرکردن انرژی طیفی کلاس 2 در فرکانس‌های بالا دنبال می‌شود. با طراحی حوزه تبدیل بهینه، داده‌ها از حوزه فراوانی به حوزه فوریه نگاشت می‌شوند. با این تبدیل بهینه، جداسازی الگوهای دوکلاسی از مفاهیم خوش‌بینی و بدبینی در حوزه تبدیل به راحتی امکان‌پذیر خواهد بود. برای محقق‌شدن مدل ریاضی، استراتژی استفاده از پروفایل نمونه‌ها روی همه نمونه‌های سیگنال نماینده کلاس 1 ارائه شده و مسأله حل می‌شود. طیف این پروفایل دارای مؤلفه‌های فرکانس پایین می‌باشد که با فرض تضاد طیفی دوکلاسی 1 و 2، حداکثرکردن انرژی طیفی کلاس 2 نیز ارضا می‌گردد. این روش به روی متون با زبان فارسی و انگلیسی اجرا شده است.
With development of web-based interactions such as social networks, personal blogs, surveys and user comments, sentiment analysis and opinion mining has become an important research domain in computer science. Up to now, many approaches have been proposed for analysis of sense using machine learning and natural language processing techniques. In this paper, we used the distribution of words in the collection of documents as new criteria for analyzing sentiment. In proposed approach, we model an optimal transform domain over words distribution with two goals: maximizing spectral energy of class at low frequencies and maximizing spectral energy of at high frequencies. Using optimal transform domain, we can map data from frequency domain into Fourier domain and easily distinguish optimism and pessimism patterns. For this purpose, we use samples’ profiles of class which have low-frequency components. Assuming the contrast of the spectrum of two classes and, maximizing the spectral energy of class will be satisfied. We have performed this approach for English and Persian documents.
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