LSTM-CNN Deep Learning Model for Sentiment Analysis of Dialectal Arabic pdf
ملخص الدراسة:
In this paper we investigate the use of Deep Learning (DL) methods for Dialectal Arabic Sentiment Analysis. We propose a DL model that combines long-short term memory (LSTM) with convolutional neural networks (CNN). The proposed model performs better than the two baselines. More specifically, the model achieves an accuracy between 81% and 93% for binary classification and 66% to 76% accuracy for three-way classification. The model is currently the state of the art in applying DL methods to Sentiment Analysis in dialectal Arabic.
توثيق المرجعي (APA)
خصائص الدراسة
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المؤلف
Abu Kwaik, Kathrein
Saad, Motaz K
Chatzikyriakidis, Stergios
Dobnik, Simon
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سنة النشر
2019
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الناشر:
Springer Science and Business Media LLC
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المصدر:
المستودع الرقمي للجامعة الإسلامية بغزة
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نوع المحتوى:
Journal Article
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اللغة:
English
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محكمة:
نعم
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الدولة:
فلسطين
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النص:
دراسة كاملة
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نوع الملف:
pdf