2020 16th International Computer Engineering Conference (ICENCO) 2020
DOI: 10.1109/icenco49778.2020.9357377
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Word Embeddings and Neural Network Architectures for Arabic Sentiment Analysis

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Cited by 6 publications
(3 citation statements)
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“…While compared to document-level sentiment analysis, this approach provides a more granular sentiment analysis. For sentence-level sentiment analysis, machine learning methods such as naive Bayes (NB), support vector machine (SVM), and deep learning models, e.g., recurrent neural networks (RNNs) and convolutional neural networks (CNNs), can be utilised [26]. Handling negation, sarcasm, and irony, and dealing with domain-specific language and jargon are typical challenges of sentence-level sentiment analysis.…”
Section: (B)mentioning
confidence: 99%
“…While compared to document-level sentiment analysis, this approach provides a more granular sentiment analysis. For sentence-level sentiment analysis, machine learning methods such as naive Bayes (NB), support vector machine (SVM), and deep learning models, e.g., recurrent neural networks (RNNs) and convolutional neural networks (CNNs), can be utilised [26]. Handling negation, sarcasm, and irony, and dealing with domain-specific language and jargon are typical challenges of sentence-level sentiment analysis.…”
Section: (B)mentioning
confidence: 99%
“…Experiments were conducted using Arabic Health Services Dataset (Main-AHS and Sub-AHS). Mohamed Fawzy et al [ 32 ] discussed a variety of DL network architectures used for Arabic sentiment classification coupled along with the word embedding approaches. RNN, CNN, Bidirectional Multi-Layer LSTM with different word embedding to do experiments.…”
Section: Related Workmentioning
confidence: 99%
“…The Arabic Health Services Dataset was used to conduct the tests. Mohamed Fawzy et al [32] stated a diversity of deep learning network models for classifying Arabic sentiment, as well as word embedding techniques. To conduct experiments, we used RNN, CNN, and Bidirectional Multi-Layer-LSTM with various word embedding.…”
Section: Literature Reviewmentioning
confidence: 99%