## 4 Proposed Hate Speech Detection Architecture
### Proposed Odia-English Text Preprocessing
#### 4.1.1 Removing (Cleaning) Irrelevant Character, Punctuations Symbol, and Emoji's
The result of each test accuracy score for the SVM, NB, and RF models based on extracted feature vectors are presented in Tables 7-9, respectively. Table 7 shows the accuracy scores of SVM model based on feature with corresponding fold tests. The average accuracy of the results recorded by the TF-IDF feature model is low, at 69.84%; while the accuracy of the results using the word2 vec feature is high, at 72.54%. Table 8 shows the prediction accuracy scores of the NB model on each feature extracted with the corresponding fold tests. The lower average accuracy of the 70.78% result was recorded on the word2 vec feature model and the slightly higher accuracy of 74.66% was recorded using the combined n-gram feature. Table 9 shows the prediction accuracy scores of the RF model on the extracted features with corresponding each fold test. The lower average accuracy of the 71.5% result was recorded on the TF-IDF with the combined n-gram feature. The higher accuracy of 75.39% was recorded on the model using the word2 vec features. The bar chart of Figure 5 visualizes the 5-fold CV average accuracy of each model based on the features in the above three tables (Tables 7-9) for binary class experiments. It reveals that the average accuracy of the NB models is slightly greater than that of the SVM and RF models based on the n-grams feature and TF-IDF combined with n-grams. In addition, the RF obtains higher accuracy using word2 vec and the TF-IDF feature. \begin{table}
\begin{tabular}{c c} \hline \hline
**Features Extraction Method** & **Features Vectors Size** \\ \hline Word unigram & 10,282 \\ Word bigram & 14,298 \\ Word trigram & 14,830 \\ Word unigram + bigram + trigram (combined n-grams) & 39,410 \\ TF-IDF & 10,279 \\ TF-IDF + combined n-grams & 49,692 \\ Word2 vec & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of the extracted features vector size. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{5}{c}{**5-Folds Accuracy (\%) for SVM Model**} & \multicolumn{1}{c}{**Average**} \\ \cline{2-7}  & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** & **Accuracy** \\ \hline Word unigram & 72.12 & 70.72 & 68.10 & 73.27 & 73.07 & 71.46 \\ Word bigrams & 73.32 & 71.62 & 68.50 & 73.47 & 72.97 & 71.98 \\ Word trigrams & 73.22 & 71.52 & 68.40 & 73.47 & 73.07 & 71.94 \\ Combined n-grams & 72.82 & 70.72 & 68.20 & 72.27 & 73.47 & 71.50 \\ TF-IDF & 70.02 & 70.22 & 66.80 & 71.67 & 70.47 & 69.84 \\ TF-IDF+ Combined n-gram & 70.12 & 70.22 & 67.20 & 71.57 & 70.67 & 69.96 \\ Word2 vec & 73.12 & 71.42 & 70.10 & 73.67 & 74.37 & 72.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SVM models’ accuracy scores for each feature using the binary class dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{5}{c}{**5-Folds Accuracy (\%) for NB Model**} & \multicolumn{1}{c}{**Average**} \\ \cline{2-7}  & **1 st** & **2 nd** & **3 rd** & **4 th** & **5 th** & **Accuracy** \\ \hline Word unigram & 73.72 & 74.52 & 74.2 & 75.27 & 74.97 & 74.54 \\ Word bigrams & 74.62 & 74.22 & 72.80 & 75.97 & 75.37 & 74.60 \\ Word trigrams & 74.72 & 74.22 & 72.90 & 75.97 & 75.27 & 74.62 \\ Combined n-grams & 74.72 & 74.82 & 72.30 & 76.07 & 75.37 & 74.66 \\ TF-IDF & 70.62 & 71.52 & 71.40 & 73.97 & 73.77 & 72.26 \\ TF-IDF+ Combined n-gram & 73.12 & 72.72 & 71.90 & 75.27 & 75.37 & 73.68 \\ Word2 vec & 71.32 & 67.33 & 69.5 & 72.97 & 72.77 & 70.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: NB models’ accuracy scores on each feature using binary class the dataset. Figure 5: Binary models’ comparisons using CV average accuracy.