# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 3 Proposed Methodology
### Data Preprocessing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Model** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{Homosexual} & Proposed method & 0.930 & 0.936 & 0.933 \\  & Logistic Regression & 0.734 & 0.698 & 0.716 \\  & SVM & 0.749 & 0.719 & 0.734 \\  & MLP & 0.765 & 0.750 & 0.757 \\  & LSTM & 0.780 & 0.770 & 0.775 \\  & Counterfactual data augmentation & 0.785 & 0.775 & 0.780 \\  & Adversarial training & 0.802 & 0.785 & 0.793 \\  & Bias-Product-of-Experts (BPoE) & 0.795 & 0.782 & 0.788 \\  & Bias-Regularized Neural Network & 0.805 & 0.790 & 0.797 \\  & RoBERTa & 0.920 & 0.915 & 0.917 \\  & DistilBERT & 0.912 & 0.905 & 0.909 \\  & BiasFinder & 0.915 & 0.920 & 0.917 \\ \hline \multirow{8}{*}{Bisexual} & Proposed method & 0.931 & 0.937 & 0.934 \\  & Logistic Regression & 0.734 & 0.698 & 0.716 \\  & SVM & 0.749 & 0.719 & 0.734 \\  & MLP & 0.765 & 0.750 & 0.757 \\  & BiasFinder & 0.915 & 0.920 & 0.917 \\ \hline \multirow{8}{*}{Other} & Proposed method & 0.932 & 0.938 & 0.935 \\  & Logistic Regression & 0.734 & 0.698 & 0.716 \\  & SVM & 0.749 & 0.719 & 0.734 \\  & MLP & 0.765 & 0.750 & 0.757 \\  & LSTM & 0.780 & 0.770 & 0.775 \\  & Counterfactual data augmentation & 0.785 & 0.775 & 0.780 \\  & Adversarial training & 0.802 & 0.785 & 0.793 \\  & Bias-Product-of-Experts (BPoE) & 0.795 & 0.782 & 0.788 \\  & BiasFinder & 0.915 & 0.920 & 0.917 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Bias evaluation results for different religion groups. \begin{table}
\begin{tabular}{c c c c c} \hline
**Group** & **Model** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{Jewish} & Proposed method & 0.932 & 0.938 & 0.935 \\  & Logistic Regression & 0.734 & 0.698 & 0.716 \\  & SVM & 0.751 & 0.719 & 0.735 \\  & MLP & 0.762 & 0.748 & 0.755 \\  & LSTM & 0.781 & 0.769 & 0.775 \\  & Counterfactual data augmentation & 0.784 & 0.771 & 0.778 \\  & Adversarial training & 0.798 & 0.783 & 0.791 \\  & Bias-Product-of-Experts (BPoE) & 0.792 & 0.779 & 0.786 \\  & Bias-Regularized Neural Network & 0.803 & 0.788 & 0.796 \\  & RoBERTa & 0.919 & 0.926 & 0.922 \\  & DistilBERT & 0.915 & 0.921 & 0.918 \\  & BiasFinder & 0.920 & 0.927 & 0.923 \\ \hline \multirow{8}{*}{Muslim} & Proposed method & 0.933 & 0.939 & 0.936 \\  & Logistic Regression & 0.734 & 0.698 & 0.716 \\  & SVM & 0.751 & 0.719 & 0.735 \\  & DistilBERT & 0.762 & 0.748 & 0.755 \\  & BiasFinder & 0.921 & 0.928 & 0.924 \\ \hline \multirow{8}{*}{Hindu} & Proposed method & 0.931 & 0.937 & 0.934 \\  & Logistic Regression & 0.734 & 0.698 & 0.716 \\  & SVM & 0.751 & 0.719 & 0.735 \\  & DistilBERT & 0.762 & 0.748 & 0.755 \\  & DistilBERT & 0.914 & 0.921 & 0.917 \\  & BiasFinder & 0.919 & 0.927 & 0.923 \\ \hline \multirow{8}{*}{Buddhist} & Proposed method & 0.930 & 0.936 & 0.933 \\  & Logistic Regression & 0.734 & 0.698 & 0.716 \\  & SVM & 0.751 & 0.719 & 0.735 \\  & MLP & 0.762 & 0.748 & 0.755 \\  & LSTM & 0.781 & 0.769 & 0.775 \\  & Counterfactual data augmentation & 0.784 & 0.771 & 0.778 \\  & Adversarial training & 0.798 & 0.783 & 0.791 \\  & Bias-Product-of-Experts (BPoE) & 0.792 & 0.779 & 0.786 \\  & Bias-Regularized Neural Network & 0.803 & 0.788 & 0.796 \\  & RoBERTa & 0.919 & 0.926 & 0.922 \\  & DistilBERT & 0.915 & 0.921 & 0.918 \\  & BiasFinder & 0.920 & 0.927 & 0.923 \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._