A Simple and Efficient Ensemble Classifier Combining Multiple Neural Network Models on Social Media Datasets in Vietnamese

• URL: http://arxiv.org/abs/2009.13060v2
• Date: Tue, 29 Sep 2020 01:32:26 GMT
• Title: A Simple and Efficient Ensemble Classifier Combining Multiple Neural Network Models on Social Media Datasets in Vietnamese
• Authors: Huy Duc Huynh, Hang Thi-Thuy Do, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
• Abstract summary: This study aims to classify Vietnamese texts on social media from three different Vietnamese benchmark datasets. Advanced deep learning models are used and optimized in this study, including CNN, LSTM, and their variants. Our ensemble model achieves the best performance on all three datasets.
• Score: 2.7528170226206443
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Text classification is a popular topic of natural language processing, which has currently attracted numerous research efforts worldwide. The significant increase of data in social media requires the vast attention of researchers to analyze such data. There are various studies in this field in many languages but limited to the Vietnamese language. Therefore, this study aims to classify Vietnamese texts on social media from three different Vietnamese benchmark datasets. Advanced deep learning models are used and optimized in this study, including CNN, LSTM, and their variants. We also implement the BERT, which has never been applied to the datasets. Our experiments find a suitable model for classification tasks on each specific dataset. To take advantage of single models, we propose an ensemble model, combining the highest-performance models. Our single models reach positive results on each dataset. Moreover, our ensemble model achieves the best performance on all three datasets. We reach 86.96% of F1- score for the HSD-VLSP dataset, 65.79% of F1-score for the UIT-VSMEC dataset, 92.79% and 89.70% for sentiments and topics on the UIT-VSFC dataset, respectively. Therefore, our models achieve better performances as compared to previous studies on these datasets.

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