Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews
| Autor: | Mohammad AL-Smadi, Omar Qawasmeh, Mahmoud Al-Ayyoub, Brij B. Gupta, Yaser Jararweh |
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| Rok vydání: | 2018 |
| Předmět: |
General Computer Science
Arabic Computer science business.industry Deep learning Sentiment analysis 020206 networking & telecommunications 02 engineering and technology computer.software_genre Machine learning language.human_language Theoretical Computer Science Support vector machine Task (computing) Identification (information) Recurrent neural network Modeling and Simulation 0202 electrical engineering electronic engineering information engineering language 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing Word (computer architecture) |
| Zdroj: | Journal of Computational Science. 27:386-393 |
| ISSN: | 1877-7503 |
| DOI: | 10.1016/j.jocs.2017.11.006 |
| Popis: | In this research, state-of-the-art approaches based on supervised machine learning are presented to address the challenges of aspect-based sentiment analysis (ABSA) of Arabic Hotels’ reviews. Two approaches of deep recurrent neural network (RNN) and support vector machine (SVM) are implemented and trained along with lexical, word, syntactic, morphological, and semantic features. The proposed approaches are evaluated using a reference dataset of Arabic Hotels’ reviews. Evaluation results show that the SVM approach outperforms the other deep RNN approach in the research investigated tasks (T1: aspect category identification, T2: aspect opinion target expression (OTE) extraction, and T3: aspect sentiment polarity identification). Whereas, when focusing on the execution time required for training and testing the models, the deep RNN execution time was faster, especially for the second task. |
| Databáze: | OpenAIRE |
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