# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 3 Proposed Methodology
### Data Preprocessing
and P.W. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The Agencia Nacional de Investigacion y Desarrollo (ANID) FONDECYT Regular grant number 1220556, ANID FOYI230169, Fondap SERC 1523A0006, the Research Project PINV01-743 of the Consejo Nacional de Ciencia y Tecnologia (CONACYT), ENNOBLE-R02401 and IRCF 39174357 project from the University of Nottingham, and IND/CONT/G/23-24/14- British Council Going Global Partnerships- Industry Academia Collaborative Grant. The authors declare no conflicts of interest. Bias evaluation results for different demographic groups. ## Appendix A \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Model** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{Asian} & Proposed method & 0.924 & 0.928 & 0.926 \\  & Logistic Regression & 0.735 & 0.710 & 0.723 \\  & SVM & 0.762 & 0.740 & 0.751 \\  & MLP & 0.775 & 0.763 & 0.769 \\  & LSTM & 0.789 & 0.779 & 0.783 \\  & RoBERTa & 0.853 & 0.839 & 0.846 \\  & DistilBERT & 0.840 & 0.830 & 0.835 \\  & 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 \\  & BiasFinder & 0.818 & 0.808 & 0.813 \\ \hline \multirow{8}{*}{Black} & Proposed method & 0.932 & 0.938 & 0.935 \\  & Logistic Regression & 0.730 & 0.710 & 0.720 \\  & SVM & 0.760 & 0.740 & 0.750 \\  & MLP & 0.773 & 0.761 & 0.767 \\  & LSTM & 0.786 & 0.776 & 0.780 \\  & RoBERTa & 0.855 & 0.840 & 0.847 \\  & DistilBERT & 0.845 & 0.835 & 0.840 \\  & 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 \\  & BiasFinder & 0.821 & 0.811 & 0.816 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Bias evaluation results for different demographic groups. \begin{table}
\begin{tabular}{c c c c c} \hline
**Group** & **Model** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{Male} & Proposed method & 0.931 & 0.937 & 0.934 \\  & Logistic Regression & 0.734 & 0.698 & 0.716 \\  & SVM & 0.742 & 0.717 & 0.729 \\  & MLP & 0.758 & 0.739 & 0.748 \\  & LSTM & 0.770 & 0.762 & 0.766 \\  & Counterfactual data augmentation & 0.776 & 0.761 & 0.768 \\  & Adversarial training & 0.789 & 0.772 & 0.780 \\  & Bias-Product-of-Experts (BPoE) & 0.783 & 0.765 & 0.774 \\  & Bias-Regularized Neural Network & 0.794 & 0.780 & 0.787 \\  & RoBERTa & 0.926 & 0.932 & 0.929 \\  & DistilBERT & 0.922 & 0.928 & 0.925 \\  & BiasFinder & 0.921 & 0.926 & 0.923 \\ \hline \end{tabular}
\end{table} 
Table 2: Bias evaluation results for different gender groups. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Model** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{8}{*}{Heterosexual} & Proposed method & 0.933 & 0.939 & 0.936 \\  & Logistic Regression & 0.734 & 0.698 & 0.716 \\  & 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 \hline \end{tabular}
\end{table} 
Table 3: Bias evaluation results for different sexual orientation groups.