Abstract
In this study, we investigate various deep learning models based on convolutional neural networks (CNNs) and Long Short Term Memory (LSTM) recurrent neural networks for sentiment analysis of Arabic microblogs. Unlike English, the Arabic language has several specifics which complicate the process of feature extraction by traditional methods. We adopted a neural language model created at Google, known as word2vec, for vectorizing text. We then designed and evaluated several deep learning architectures using CNN and LSTM. The experiments were run on two publicly available Arabic tweets datasets. Promising results have been attained when combining LSTMs and compared favorably with most related work.
Original language | English |
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Title of host publication | Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings |
Editors | Dongbin Zhao, El-Sayed M. El-Alfy, Derong Liu, Shengli Xie, Yuanqing Li |
Publisher | Springer Verlag |
Pages | 491-500 |
Number of pages | 10 |
ISBN (Print) | 9783319700953 |
DOIs | |
State | Published - 2017 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10635 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Bibliographical note
Publisher Copyright:© 2017, Springer International Publishing AG.
Keywords
- Arabic sentiment analysis
- Convolutional neural network
- Deep learning
- Long short-term memory
- Word embedding
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science