## 4 Proposed Hate Speech Detection Architecture
### Proposed Odia-English Text Preprocessing
#### 4.1.1 Removing (Cleaning) Irrelevant Character, Punctuations Symbol, and Emoji's
Figure 10 shows the confusion matrix for the SVM models. The SVM model with trigram classifies 43% of HS, 60% of OFS and 45% of the neither (OK) classes correctly, but 48% of HS and 45% of the OK class were misclassified as OFS. The misclassification rate of the HS and OK classes is 10% and 9%, respectively. The SVM model with TF-IDF+n-grams classifies 45% of HS, 52% of OFS and 48% of the OK class correctly, but 41% of HS and 38% of the OK class were misclassified as OFS. The misclassification rate of the HS and OK classes is 15% and 14%, respectively. Similarly, the SVM model with word2 vec classifies 46% of HS, 49% of OFS and 65% of the OK class correctly, but 42% of HS and 28% of the OK class were misclassified as OFS. The misclassification rate of the HS and OK classes are 12% and 7%, respectively. Figure 11 shows a confusion matrix of the NB models. The NB model with combined n-gram classifies 50% of HS, 58% of OFS and 36% of the OK class correctly, but 46% of HS and 47% of the OK class were misclassified as OFS. The misclassification rate of the HS and OK classes is 4% and 17%, respectively. The NB model with TF-IDF+n-grams classifies 50% of HS, 54% of OFS and 39% of the OK class correctly, but 44% of HS and 42% of the OK class were misclassified as OFS. The misclassification rate of the HS and OK classes are 7% and 19%, respectively. In addition, the NB model with word2 vec classifies 55% of HS, 38% of OFS and 63% of the OK class correctly, but 28% of HS and 26% of the OK class were misclassified as OFS. The misclassification rate of the HS and OK classes is 11% and 17%, respectively. Figure 12 shows the confusion matrix of the RF models. The RF model with unigram classifies 33% of HS, 64% of OFS and 46% of the OK class correctly, but 54% of HS and 48% of the OK class were misclassified as OFS. The misclassification rate of the HS and OK classes are 6% and 13%, respectively. The RF model with TF-IDF classifies 31% of HS, 65% of OFS and 46% of the OK class correctly, but 55% of HS and 47% of the OK class were misclassified as OFS. The misclassification rate of the HS and OK classes is 6% and 13%, respectively. In addition, the RF model with word2 vec classifies 18% of HS, 83% of OFS and 40% of the OK class correctly, but 78% of HS and 59% of the OK class were misclassified as OFS. The misclassification rate of the HS and OK classes is 3% and 2%, respectively. Normalized confusion matrices for ternary classifier models based on the feature extracted are represented in Figures 10-12, exhibiting the classification results of a 5-fold CV of each model. The actual class is the label's post or comment in a dataset and the predicted class is the prediction labels by the models. The classes are hate, offensive and neither (OK). Conclusively, the SVM model with word2 vec produced slightly better classification results than the NB and RF models. Figure 10: Confusion matrix of sample ternary SVM models based on extracted features. ### Comparision with Results of Conventional ML Methods

In this section, the authors use four conventional machine learning (ML) techniques: logistic regression (LR), decision tree (DT), gradient boosting (GB) and K-nearest neighbor (KNN) for the classification of text. For comparison, the authors used the exact same dataset of Odia-English code mixed data devised by themselves. The authors reinforced the models using three classes of data: hate, offensive and neither (OK). First, the authors investigated using the unigram feature based on ML methods, followed by the bigram and