Presenting an approach based on weighted CapsuleNet networks for Arabic and Persian multi-domain sentiment analysis
Publish Year: 1403
نوع سند: مقاله ژورنالی
زبان: English
View: 61
This Paper With 14 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJNAA-15-5_021
تاریخ نمایه سازی: 18 فروردین 1403
Abstract:
Sentiment classification is a fundamental task in natural language processing, assigning one of the three classes, positive, negative, or neutral, to free texts. However, sentiment classification models are highly domain dependent; the classifier may perform classification with reasonable accuracy in one domain but not in another due to the Semantic multiplicity of words getting poor accuracy. This article presents a new Persian/Arabic multi-domain sentiment analysis method using the cumulative weighted capsule networks approach. Weighted capsule ensemble consists of training separate capsule networks for each domain and a weighting measure called domain belonging degree (DBD). This criterion consists of TF and IDF, which calculates the dependency of each document for each domain separately; this value is multiplied by the possible output that each capsule creates. In the end, the sum of these multiplications is the title of the final output, and is used to determine the polarity. And the most dependent domain is considered the final output for each domain. The proposed method was evaluated using the Digikala dataset and obtained acceptable accuracy compared to the existing approaches. It achieved an accuracy of ۰.۸۹ on detecting the domain of belonging and ۰.۹۹ on detecting the polarity. Also, for the problem of dealing with unbalanced classes, a cost-sensitive function was used. This function was able to achieve ۰.۰۱۶۲ improvements in accuracy for sentiment classification. This approach on Amazon Arabic data can achieve ۰.۹۶۹۵ accuracies in domain classification
Keywords:
Authors
Sanaz Gouran Shourakchali
Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
Kamran Layeghi
Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
Faraein Aeini
Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :