Subword Attentive Model for Arabic Sentiment Analysis
| Autor: | Haytham H. Elmousalami, Majdi Beseiso |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Computer science business.industry 020209 energy Deep learning Big data Unstructured data 02 engineering and technology computer.software_genre Semantics Convolutional neural network Recurrent neural network 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Social media Artificial intelligence business Social network analysis computer Natural language processing |
| Zdroj: | ACM Transactions on Asian and Low-Resource Language Information Processing. 19:1-17 |
| ISSN: | 2375-4702 2375-4699 |
| DOI: | 10.1145/3360016 |
| Popis: | Social media data is unstructured data where these big data are exponentially increasing day to day in many different disciplines. Analysis and understanding the semantics of these data are a big challenge due to its variety and huge volume. To address this gap, unstructured Arabic texts have been studied in this work owing to their abundant appearance in social media Web sites. This work addresses the difficulty of handling unstructured social media texts, particularly when the data at hand is very limited. This intelligent data augmentation technique that handles the problem of less availability of data are used. This article has proposed a novel architecture for hand Arabic words classification and understands based on convolutional neural networks (CNNs) and recurrent neural networks. Moreover, the CNN technique is the most powerful for the analysis of Arabic tweets and social network analysis. The main technique used in this work is character-level CNN and a recurrent neural network stacked on top of one another as the classification architecture. These two techniques give 95% accuracy in the Arabic texts dataset. |
| Databáze: | OpenAIRE |
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