Integrated bidirectional LSTM–CNN model for customers reviews classification | ||
| Journal of Engineering Science and Military Technologies | ||
| Article 2, Volume 5, Issue 1, March 2021 PDF (1.27 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ejmtc.2021.66626.1172 | ||
| Authors | ||
| Hossam elzayady* 1; Mohamed sobhy2; khale badran3 | ||
| 1computer engineering , MTC | ||
| 2Computer Engineering ,MTC | ||
| 3Computer Engineering , MTC | ||
| Abstract | ||
| The tremendous increase of Internet users and various social media platforms provide a massive amount of data. Companies are seeking an automated method to assess their customers' satisfaction with their products. Collecting and analyzing opinions and customers' feedback from social media rely on what so called sentiment classification. Several types of research are carried out to investigate opinions in English. As the Arabic language analysis faces many numerous challenges and problems. In our current research, two powerful hybrid deep learning models (CNN-LSTM) and (CNN- BILSTM) are represented. Bidirectional LSTMs are an expansion of conventional LSTMs that can make substantial improvements in sequence classification tasks and identify the most valuable features, CNN is applied. Various data preparation processes are performed, and two regular deep learning models (CNN, LSTM) are implemented to conduct a series of experiments. Experimental results show that the two proposed models have superior performance compared to baselines deep learning models (CNN, LSTM). Furthermore, the (CNN-BI-LSTM) model exceeds the hybrid (CNN-LSTM) model in terms of achieving the highest efficiency. | ||
| Keywords | ||
| Bi-directional long shortterm memory (BILSTM); deep learning; convolutional neural network (CNN) | ||
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