Comparison Between Traditional Machine Learning Models And Neural Network Models For Vietnamese Hate Speech Detection

Luu, Son T.; Nguyen, Huan Buu; Nguyen, Kiet Van; Nguyen, Ngan Luu-Thuy

doi:10.1109/rivf48685.2020.9140745

Cited by 22 publications

(18 citation statements)

References 8 publications

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“…Table 6 shows the result by F1-macro score. Comparing with the original results (Luu et al, 2020), the accuracy of the HSD-VSLP dataset after using augmented techniques are higher than the original dataset. According to Figure 4, the number of right prediction on the offensive and the hate labels are increased.…”

Section: Model Performance Resultsmentioning

confidence: 74%

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Empirical Study of Text Augmentation on Social Media Text in Vietnamese

Luu¹,

Nguyen²,

Nguyen³

2020

Preprint

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In the text classification problem, the imbalance of labels in datasets affect the performance of the text-classification models. Practically, the data about user comments on social networking sites not altogether appeared -the administrators often only allow positive comments and hide negative comments. Thus, when collecting the data about user comments on the social network, the data is usually skewed about one label, which leads the dataset to become imbalanced and deteriorate the model's ability. The data augmentation techniques are applied to solve the imbalance problem between classes of the dataset, increasing the prediction model's accuracy. In this paper, we performed augmentation techniques on the VLSP2019 Hate Speech Detection on Vietnamese social texts and the UIT -VSFC: Vietnamese Students' Feedback Corpus for Sentiment Analysis. The result of augmentation increases by about 1.5% in the F1-macro score on both corpora.

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Section: Model Performance Resultsmentioning

confidence: 74%

“…For the HSD-VLSP corpus, we use crossvalidation with five folds for the Text-CNN model and the Maximum Entropy model. Following the same manner in the previous study (Luu et al, 2020), for each fold, we keep the test set and enhance the training set with EDA techniques.…”

Section: Experiments Configurationmentioning

confidence: 99%

Empirical Study of Text Augmentation on Social Media Text in Vietnamese

Luu¹,

Nguyen²,

Nguyen³

2020

Preprint

Self Cite

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show abstract

“…On the other hand, we conducted a survey on the related works serving the task of classifying comments on social networks in Vietnamese, especially the Vietnamese Hate Speech Detection task is still modest [9,10,[19][20][21][22][23][24]. Specifically, the current studies revolve only based on two typical datasets by their outstanding high quality and large quantity of data points: ViHSD [10] and HSD-VLSP [9] datasets.…”

Section: Existing Hsd Modelsmentioning

confidence: 99%

“…We also overcome the remaining restrictions on the ViHSD and HSD-VLSP datasets in previous studies [9,10,[19][20][21][22][23][24] by proposing a two-phase data preprocessing techniques. Furthermore, we inherit the advantages of each study, such as the ability to conduct the experiment with deep learning, transfer learning models, and combined models.…”

Section: Hate Speech Detection With Streaming Datamentioning

confidence: 99%

“…The combined PhoBERT-CNN model is expected to significantly improve the classification performance thanks to the resonance mechanism of the two single models, reducing errors between the predicted labels and the actual labels. Both of the two single models, PhoBERT and Text-CNN, outperformed other models on the tasks of classifying Vietnamese text, particularly on the ViHSD and HSD-VLSP datasets [9,10,[19][20][21][22][23][24]. Figure 6 presents the architecture of our approach for Vietnamese HSD.…”

Section: Proposed Phobert-cnn Model For Hsdmentioning

confidence: 99%

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Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data

Tran¹,

Nguyen²,

Hoang³

et al. 2022

Preprint

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Society needs to develop a system to detect hate and offense to build a healthy and safe environment. However, current research in this field still faces four major shortcomings, including deficient pre-processing techniques, indifference to data imbalance issues, modest performance models, and lacking practical applications. This paper focused on developing an intelligent system capable of addressing these shortcomings. Firstly, we proposed an efficient pre-processing technique to clean comments collected from Vietnamese social media. Secondly, a novel hate speech detection (HSD) model, which is the combination of a pretrained PhoBERT model and a Text-CNN model, was proposed for solving tasks in Vietnamese. Thirdly, EDA techniques are applied to deal with imbalanced data to improve the performance of classification models. Besides, various experiments were conducted as baselines to compare and investigate the proposed model's performance against state-of-the-art methods. The experiment results show that the proposed PhoBERT-CNN model outperforms SOTA methods and achieves an F1-score of 67,46% and 98,45% on two benchmark datasets, ViHSD and HSD-VLSP, respectively. Finally, we also built a streaming HSD application to demonstrate the practicality of our proposed system.

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An Empirical Experiment on Feature Extractions Based for Speech Emotion Recognition

Duong

Nguyễn

et al. 2022

Intelligent Information and Database Systems

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Comparison Between Traditional Machine Learning Models And Neural Network Models For Vietnamese Hate Speech Detection

Cited by 22 publications

References 8 publications

Empirical Study of Text Augmentation on Social Media Text in Vietnamese

Empirical Study of Text Augmentation on Social Media Text in Vietnamese

Vietnamese Hate and Offensive Detection using PhoBERT-CNN and Social Media Streaming Data

An Empirical Experiment on Feature Extractions Based for Speech Emotion Recognition

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