Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels' reviews
By Mohammad Al-Smadi, Omar Qawasmeh, Mahmoud Al-Ayyoub, Y. Jararweh, Brij Bhooshan Gupta. Journal of Computer Science, 2017
312 citations recorded by the source corpus.
Abstract 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.
Subfields: Artificial Intelligence, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/b1b35866e1e3d0cc7201f3417fa1659eceedfb47
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In Artificial Intelligence
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In Management Science and Operations Research
Citations in the corpus
- Sentiment analysis using deep learning architectures: a review (2019, methodology): “…Bayes (Books) Naïve Bayes (Camera) Naïve Bayes (Music) 80 (multiword level feature) 80 (single word level feature) 80 (single word level feature) 260 Al-Smadi et al. (2017) Arabic Hotels’ Reviews (Al-Smadi et al. 2016; Pontiki 2016) For aspect-based sentiment analysis tasks Deep RNN SVM For…”
- A survey on sentiment analysis methods, applications, and challenges (2022, background): “(Sann and Lai 2020; Al-Smadi et al. 2018) According to sentiment analysis, this is one of the most attractive industries.”
- Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review (2019, background): “(2017) and Al-Smadi et al. (2017), when compared to conventional machine learning, time is still a current issue for DNN models.”