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Register a new userUsing Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Social Media Product Reviews
استخدام Valence و BI-LSTM الثابتة المتزايدة لتحليل المعنويات في مراجعات منتجات الوسائط الاجتماعية
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مع شعبية عمر الإنترنت الحالي، قدمت المنصات الاجتماعية عبر الإنترنت جسر للتواصل بين الشركات الخاصة والمؤسسات العامة والجمهور.الغرض من هذا البحث هو فهم تجربة المستخدم للمنتج من خلال تحليل بيانات مراجعة المنتجات في حقول مختلفة.نقترح شبكة عصبية مقرها Bilstm والتي غزت المعلومات العاطفية الغنية.بالإضافة إلى النظر في التكافؤ والإثارة وهو أصغر المعلومات العاطفية، يتم دمج علاقة الاعتماد بين النصوص أيضا في نموذج التعلم العميق لتحليل المعنويات.تظهر النتائج التجريبية أن هذا البحث يمكن أن يحقق أداء جيدا في التنبؤ بمفردات التكافؤ والإثارة.بالإضافة إلى ذلك، يمكن أن يكون دمج معلومات VA والاعتماد في نموذج Bilstm أداء ممتاز لتحليل معنويات النص الاجتماعي، والذي يتحقق من أن هذا النموذج فعال في الاعتراف بالمشاعر النص الإنسي الاجتماعي الاجتماعي.
With the popularity of the current Internet age, online social platforms have provided a bridge for communication between private companies, public organizations, and the public. The purpose of this research is to understand the user's experience of the product by analyzing product review data in different fields. We propose a BiLSTM-based neural network which infused rich emotional information. In addition to consider Valence and Arousal which is the smallest morpheme of emotional information, the dependence relationship between texts is also integrated into the deep learning model to analyze the sentiment. The experimental results show that this research can achieve good performance in predicting the vocabulary Valence and Arousal. In addition, the integration of VA and dependency information into the BiLSTM model can have excellent performance for social text sentiment analysis, which verifies that this model is effective in emotion recognition of social medial short text.
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