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

A Simple and Efficient Ensemble Classifier Combining Multiple Neural Network Models on Social Media Datasets in Vietnamese Huy Duc Huynh University of Information Technology VNU-HCM, Vietnam 16520508@gm.uit.edu.vn Hang Thi-Thuy Do University of Information Technology VNU-HCM, Vietnam 16520339@gm.uit.edu.vn Kiet Van Nguyen University of Information Technology VNU-HCM, Vietnam kietnv@uit.edu.vn Ngan Thuy-Luu Nguyen University of Information Technology VNU-HCM, Vietnam ngannlt@uit.edu.vn Abstract Text classification is a popular topic of natural language processing, which has currently at- tracted numerous research efforts worldwide. The significant increase of data in social me- dia requires the vast attention of researchers to analyze such data. There are various stud- ies in this field in many languages but lim- ited 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 re- sults on each dataset. Moreover, our ensem- ble 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. There- fore, our models achieve better performances as compared to previous studies on these datasets. 1 Introduction The rapid development of science and technology in the world has created a vast amount of data. In particular, the growth of social networks continu- ously creates a huge amount of comments and posts which are valuable sources to exploit and analyze in the digital era. Text classification is a prerequisite for such works as analyzing user opinion in the net- work environment, filtering and removing malicious information, and detecting criminal risk. With great potential, text classification has attracted much at- tention from experts in the natural language process- ing community worldwide. In English, we easily search for a range of text classification publications in many fields. However, relatively few researches have been done on Vietnamese text. Most published articles focus on binary classification. However, a large amount of information today requires analysis in many more aspects (multi-label or multi-class). The lack of knowledge and techniques for the Viet- namese language makes us decide to conduct this research to classify multi-class text for Vietnamese social media datasets. These datasets are provided from the VLSP share-task and publications on text classification. In particular, there are various so- cial media textual datasets such as UIT-VSMEC for emotion recognition (Ho et al., 2019) and UIT- VSFC for students’ feedback classification (Nguyen et al., 2018b) and HSD-VLSP for hate speech de- tection (Vu et al., 2019). These are the datasets with multi-label and imbalance between the labels that have been published recently. They are suitable for …

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