Applying Recurrent Networks For Arabic Sentiment Analysis | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 3, Volume 31, Issue 1, January 2022, Page 21-28 PDF (1.5 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2022.218776 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Eslam Omara  1; Mervat Mosa2; Nabil Ismail2
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| 1Cairo Governorate | ||||
| 2Computer Science and Engineering - Faculty of Electronic Engineering - Menofia University - Menouf - Egypt | ||||
| Abstract | ||||
| The main characteristic of deep learning approaches is the ability to learn differentiating and discriminating features. These techniques can discover complex relations and structures within high-dimensional data. For feature extraction, deep learning models employ several layers of nonlinear processing units. One of the fields that have applied deep architectures with a noticeable breakthrough in performance measures is Natural Language Processing (NLP). Recurrent neural networks (RNNs) and their variants Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are commonly used for NLP applications as they are efficient at processing sequential data. Unlike RNNs, LSTMs and GRUs can combat vanishing and exploding gradients. In Addition, Convolutional Neural Network (CNN) is another deep architecture that has been widely used in language processing. On the other side, sentiment analysis (SA) is an NLP task concerned with opinions, attitudes, emotions, and feelings. Sentiment analysis deduces the author's attitude regarding a topic and classifies the attitude polarity according to a set of predefined classes. Application of SA in business analytics helps to gain insight into consumer behaviour and needs. In the proposed work deep LSTM, GRU, and CNN are applied for Arabic sentiment analysis. The models are implemented and tested employing character-level representation. Also, deep hybrid models that combine multiple layers of CNN with LSTM or GRU are studied. The application aims at investigating the capability of deep LSTM, GRU, and hybrid architectures to learn and extract features from character-level representation. Results show that combining different architectures can boost performance in SA tasks. The CNN-LSTM/GRU combinations registered higher accuracy compared to deep LSTM and GRU. | ||||
| Keywords | ||||
| Deep learning; Sentiment analysis; LSTM; GRU; CNN-LSTM; CNN-GRU | ||||
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