Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12358/27304
Title | LSTM-CNN Deep Learning Model for Sentiment Analysis of Dialectal Arabic |
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Title in Arabic | LSTM-CNN Deep Learning Model for Sentiment Analysis of Dialectal Arabic |
Abstract |
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. |
Type | Journal Article |
Date | 2019 |
Language | English |
Subjects | |
Published in | Arabic Language Processing: From Theory to Practice |
Series | Vol. 1108 |
Publisher | Springer Science and Business Media LLC |
Citation | |
Item link | Item Link |
DOI | 10.1007/978-3-030-32959-4_8 |
ISSN | 18650929,18650937 |
ISBN | 9783030329587,9783030329594 |
License | ![]() |
Collections | |
Files in this item | ||
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ICALP_Deep_Learning.pdf | 367.5Kb |