# A Study of Discriminatory Speech Classification Based on Improved Smote and SVM-RF
## 5 Conclusions  
With the rapid development of the Internet, a large number of online remarks have been generated. The discussed web discourse represents the views and feelings of web users. However, there are some utterances that contain discriminatory meanings. In view of the potential harm of discriminatory speech, it is crucial to classify and detect discriminatory speech instances. Therefore, this paper collects and processes the data of discriminatory speech in Sina Weibo and proposes an improved SMOTE algorithm based on LDA, which filters out the high-quality data related to the topic and oversamples the imbalanced dataset to synthesize new data. Finally, the weights were calculated by the correct rates of the classification results of SVM and RF, and the final result of the model was obtained by weighted summation of the classification results of SVM and RF. The experimental results show that the proposed method improves the precision, recall, and F1 value by 6.0%, 5.4%, and 5.7%, respectively, compared with SVM, and also shows the best performance compared with other machine learning methods. In addition, the ablation experiment results show that the improved SMOTE and the integrated method can effectively improve the model. In the future, we will continue to optimize our model (e.g., model parameter tuning, feature engineering) to improve its overall performance. In addition, we also consider integrating other models (such as DT and XGB) and optimize the ensemble method, such as using the stacking method to synthesize the prediction results of each model to improve the generalization ability and performance of the model. In the face of imbalanced or sparse data, we will adopt the improved SMOTE method to solve these problems. Combined with the advantages of neural networks in dealing with large-scale and complex data, these combinations will likely improve the performance and robustness of the model. Our future research directions include exploring how different types of neural networks can be integrated and improving the experimental design to delve into how these deep learning models perform on our specific tasks and datasets. We use these models as baselines and also combine them with the improved SMOTE as well as the integrated model, respectively, to comprehensively evaluate and compare their performance.  
Conceptualization, C.W. and D.Z.; methodology, C.W. and H.H.; software, X.S.; validation, C.W. and H.H.; formal analysis, C.W. and H.H.; investigation, C.W.; resources, X.S. and D.Z.; data curation, C.W.; writing--original draft preparation, C.W.; writing--review and editing, C.W.; visualization, K.-L.Y.; supervision, A.W.H.I.; project administration, D.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: Restrictions apply to the availability of these data. The dataset presented in this article is not available because it is part of an ongoing study.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1** \\ \hline SVM & 0.843 & 0.862 & 0.852 \\ Improved SMOTE + SVM & 0.886 & 0.893 & 0.889 \\ RF & 0.873 & 0.834 & 0.853 \\ Improved SMOTE + RF & 0.881 & 0.868 & 0.859 \\ Improved SMOTE + SVM + RF & 0.903 & 0.916 & 0.909 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiment.  
**Acknowledgments:** This research is in part supported by the Research Centre of Deep Space Explorations, the Hong Kong Polytechnic University.  
**Conflicts of Interest:** The authors declare no conflicts of interest.