LSTM-CNN Deep Learning Model for Sentiment Analysis of Dialectal Arabic pdf

تفاصيل الدراسة

LSTM-CNN Deep Learning Model for Sentiment Analysis of Dialectal Arabic pdf

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

  • المؤلف

    Abu Kwaik, Kathrein

    Saad, Motaz K

    Chatzikyriakidis, Stergios

    Dobnik, Simon

  • سنة النشر

    2019

  • الناشر:

    Springer Science and Business Media LLC

  • المصدر:

    المستودع الرقمي للجامعة الإسلامية بغزة

  • نوع المحتوى:

    Journal Article

  • اللغة:

    English

  • محكمة:

    نعم

  • الدولة:

    فلسطين

  • النص:

    دراسة كاملة

  • نوع الملف:

    pdf

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