حس‌نگار : شبكه واژگان حسي فارسي

Hes Negar: Persian Sentiment WordNet

وظيفه اصلي نظركاوي استخراج و تشخيص حس مثبت يا منفي (رضايت‌مندي) افراد، از روي اطلاعات متني است. نبود يك واژه‌نامه حسي فارسي عامل يكي از چالش‌‌هاي اصلي نظركاوي در زبان فارسي است. در اين مقاله روشي جديد براي توليد شبكه واژگان حسي فارسي (حس‌نگار) با استفاده از منابع زباني فارسي و انگليسي ارائه مي‌شود. همچنين پيكره نظرات فارسي ايجاد‌شده براي انجام پژوهش‌هاي نظركاوي، معرفي خواهند‌شد. براي توليد حس‌نگار ابتدا شبكه واژگان جامع زبان فارسي (فردوس‌نت) ساخته شده‌است. سپس ميزان حس هر گروه هم‌معني در شبكه واژگان حسي انگليسي به كلمات متناظر آنها در حس‌نگار (شبكه واژگان حسي فارسي) نگاشت مي‌شود. در آزمايش‌هاي انجام‌شده، مشخص شد كه حس‌نگار داراي دقت 0/86 و نرخ بازيابي 0/75 است و مي‌تواند به عنوان واژه‌نامه حسي مرجع براي زبان فارسي استفاده شود

Awareness of others' opinions plays a crucial role in the decision making process performed by simple customers to top-level executives of manufacturing companies and various organizations. Today, with the advent of Web 2.0 and the expansion of social networks, a vast number of texts related to people's opinions have been created. However, exploring the enormous amount of documents, various opinion sources and opposing opinions about an entity have made the process of extracting and analyzing opinions very difficult. Hence, there is a need for methods to explore and summarize the existing opinions. Accordingly, there has recently been a new trend in natural language processing science called "opinion mining". The main purpose of opinion mining is to extract and detect people’s positive or negative sentiments (sense of satisfaction) from text reviews. The absence of a comprehensive Persian sentiment lexicon is one of the main challenges of opinion mining in Persian. In this paper, a new methodology for developing Persian Sentiment WordNet (HesNegar) is presented using various Persian and English resources. A corpus of Persian reviews developed for opinion mining studies are introduced. To develop HesNegar, a comprehensive Persian WordNet (FerdowsNet), with high recall and proper precision (based on Princeton WordNet), was first created. Then, the polarity of each synset in English SentiWordNet is mapped to the corresponding words in HesNegar. In the conducted tests, it was found that HesNegar has a precision score of 0.86 a recall score of 0.75 and it can be used as a comprehensive Persian SentiWordNet. The findings and developments made in this study could prove useful in the advancement of opinion mining research in Persian and other similar languages, such as Urdu and Arabic.