## 4 Experimental Results  
We have proposed three approaches for OLD using the publicly available Davidson dataset, which has three classes (hate, offensive, and neither hate nor offensive). We utilized TF-IDF, Word2 Vec, FKRA/FRE, and VADER for feature extraction, incorporating all features for model training and testing. The dataset was split into training (80%) and testing (20%) sets. The models were evaluated using standard metrics: precision (Prc), recall (Rcl), and F1-score (F1). These metrics are calculated as follows:  
\[Prec=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{29}\]  
\[Rcl=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{30}\]  
\[F1=2\times\frac{\text{Prc}\times\text{Rcl}}{\text{Prc}+\text{Rcl}} \tag{31}\]  
where TP is true positive, TN is true negative, FP is false positive, and FN represents false negative. The Davidson data are an imbalanced dataset, so we have used F1 as the performance metric of the model. We have a table showing various evaluation metrics for different models on the Davidson dataset. The models were trained with different feature combinations, including TF-IDF, VADER sentiment scores, Word2 Vec embeddings, and linguistic features (FKRA/FRE). The evaluation metrics include Prc, Rcl, and F1, both in terms of macro and weighted averages. The performance analysis using TF-IDF features, presented in Table 1, shows promising results with Weighted Average F1 scores of 0.89, 0.88, 0.70, and 0.89 for Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) classifiers, respectively. Incorporating sentiment scores with TF-IDF features enhanced model performance. Logistic Regression and Random Forest achieved F1-macro scores of 0.69 and F1-weighted scores of 0.89. Naive Bayes showed improvement with F1-macro of 0.50 and F1-weighted \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Features**} & \multicolumn{1}{c}{**Model**} & \multicolumn{3}{c}{**Macro**} & \multicolumn{3}{c}{**Weighted Avg.**} \\ \hline  & & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multicolumn{10}{c}{**Results on Davidson Dataset**} \\ \hline TF-IDF & LR & 0.77 & 0.66 & 0.68 & 0.88 & 0.90 & 0.88 \\ TF-IDF & RF & 0.77 & 0.68 & 0.69 & 0.89 & 0.91 & 0.89 \\ TF-IDF & NB & 0.51 & 0.55 & 0.50 & 0.79 & 0.65 & 0.70 \\ TF-IDF & SVM & 0.74 & 0.69 & 0.70 & 0.88 & 0.89 & 0.89 \\ TF-IDF, VADER & LR & 0.79 & 0.67 & 0.69 & 0.89 & 0.90 & 0.89 \\ TF-IDF, VADER & RF & 0.76 & 0.64 & 0.66 & 0.88 & 0.89 & 0.88 \\ TF-IDF, VADER & NB & 0.51 & 0.55 & 0.50 & 0.79 & 0.65 & 0.70 \\ TF-IDF, VADER & SVM & 0.73 & 0.69 & 0.70 & 0.88 & 0.89 & 0.88 \\ TF-IDF, VADER, Word2 Vec & LR & 0.77 & 0.66 & 0.68 & 0.88 & 0.90 & 0.88 \\ TF-IDF, VADER, Word2 Vec & RF & 0.75 & 0.60 & 0.62 & 0.86 & 0.89 & 0.86 \\ TF-IDF, VADER, Word2 Vec & NB & 0.51 & 0.55 & 0.50 & 0.79 & 0.65 & 0.70 \\ TF-IDF, VADER, Wordvec & SVM & 0.74 & 0.69 & 0.71 & 0.88 & 0.89 & 0.89 \\ TF-IDF, VADER, Word2 Vec, FKRA, FRE & LR & 0.49 & 0.46 & 0.46 & 0.75 & 0.81 & 0.77 \\ TF-IDF, VADER, Word2 Vec, FKRA, FRE & RF & 0.74 & 0.58 & 0.60 & 0.86 & 0.88 & 0.86 \\ TF-IDF, VADER, Word2 Vec, FKRA, FRE & NB & 0.53 & 0.57 & 0.51 & 0.80 & 0.66 & 0.72 \\ TF-IDF, VADER, Word2 Vec, FKRA, FRE & SVM & 0.77 & 0.61 & 0.62 & 0.87 & 0.89 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the ML models used. of 0.70, while SVM maintained strong performance with F1-macro of 0.70 and F1-weighted of 0.89. The integration of Word2 Vec embeddings with TF-IDF and VADER features produced varying performance across classifiers. LR and RF showed F1-macro scores ranging from 0.62 to 0.68 and F1-weighted scores of 0.86 to 0.88. NB maintained consistent performance (F1-Macro: 0.50, F1-Weighted: 0.70), while SVM demonstrated improved results with F1-macro of 0.71 and F1-weighted of 0.89. Our analysis of the Davidson dataset reveals that feature selection and model choice significantly influence classification performance.