Please use this identifier to cite or link to this item:
|Title||LSTM-CNN Deep Learning Model for Sentiment Analysis of Dialectal Arabic|
|Title in Arabic||LSTM-CNN Deep Learning Model for Sentiment Analysis of Dialectal Arabic|
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.
|Published in||Arabic Language Processing: From Theory to Practice|
|Publisher||Springer Science and Business Media LLC|
|Item link||Item Link|
|Files in this item|