Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models
المؤلفون المشاركون
المصدر
Discrete Dynamics in Nature and Society
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-24
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Purpose.
The purpose of this article is to predict the topic popularity on the social network accurately.
Indicator selection model for a new definition of topic popularity with degree of grey incidence (DGI) is undertook based on an improved analytic hierarchy process (AHP).
Design/Methodology/Approach.
Through screening the importance of indicators by the deep learning methods such as recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent unit (GRU), a selection model of topic popularity indicators based on AHP is set up.
Findings.
The results show that when topic popularity is being built quantitatively based on the DGI method and different weights of topic indicators are obtained from the help of AHP, the average accuracy of topic popularity prediction can reach 97.66%.
The training speed is higher and the prediction precision is higher.
Practical Implications.
The method proposed in the paper can be used to calculate the popularity of each hot topic and generate the ranking list of topics’ popularities.
Moreover, its future popularity can be predicted by deep learning methods.
At the same time, a new application field of deep learning technology has been further discovered and verified.
Originality/Value.
This can lay a theoretical foundation for the formulation of topic popularity tendency prevention measures on the social network and provide an evaluation method which is consistent with the actual situation.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Hong, Yuling& Zhang, Qishan. 2020. Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1153672
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Hong, Yuling& Zhang, Qishan. Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1153672
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Hong, Yuling& Zhang, Qishan. Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1153672
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1153672
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