Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism
المؤلفون المشاركون
Chen, Xiao-liang
Tang, Mingwei
Wang, Xiaodi
Yang, Tian
Wang, Zhen
المصدر
Discrete Dynamics in Nature and Society
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-07-08
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
The aim of aspect-level sentiment analysis is to identify the sentiment polarity of a given target term in sentences.
Existing neural network models provide a useful account of how to judge the polarity.
However, context relative position information for the target terms is adversely ignored under the limitation of training datasets.
Considering position features between words into the models can improve the accuracy of sentiment classification.
Hence, this study proposes an improved classification model by combining multilevel interactive bidirectional Gated Recurrent Unit (GRU), attention mechanisms, and position features (MI-biGRU).
Firstly, the position features of words in a sentence are initialized to enrich word embedding.
Secondly, the approach extracts the features of target terms and context by using a well-constructed multilevel interactive bidirectional neural network.
Thirdly, an attention mechanism is introduced so that the model can pay greater attention to those words that are important for sentiment analysis.
Finally, four classic sentiment classification datasets are used to deal with aspect-level tasks.
Experimental results indicate that there is a correlation between the multilevel interactive attention network and the position features.
MI-biGRU can obviously improve the performance of classification.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Xiaodi& Chen, Xiao-liang& Tang, Mingwei& Yang, Tian& Wang, Zhen. 2020. Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1153201
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Xiaodi…[et al.]. Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1153201
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Xiaodi& Chen, Xiao-liang& Tang, Mingwei& Yang, Tian& Wang, Zhen. Aspect-Level Sentiment Analysis Based on Position Features Using Multilevel Interactive Bidirectional GRU and Attention Mechanism. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1153201
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1153201
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر