# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 3 Proposed Methodology
### Data Preprocessing
### Comparison with Baseline Models

Table 3 summarizes the performance of our proposed method against baseline models (Logistic Regression, SVM, MLP, LSTM, RoBERTa, and DistilBERT) on the test set, evaluated using accuracy, precision, recall, and F1-score. Logistic regression and SVM achieved modest performance, with accuracies of 76.5% and 77.8%, respectively, reflecting their limitations in handling complex, nonlinear data. The MLP model showed better results (79.3% accuracy), leveraging its ability to capture intricate patterns, while LSTM outperformed it with 81.0% accuracy, demonstrating the benefit of modeling sequential relationships. RoBERTa and DistilBERT, both transformer-based models, also demonstrated high performance, with RoBERTa achieving 85.0% accuracy and DistilBERT reaching 84.5%. Our proposed method significantly outperformed all baseline models, achieving 92.5% accuracy and an F1-score of 92, showing a clear advantage in both accuracy and bias mitigation. ### Comparison with Existing Models in the Literature

Table 3 presents a comparison of our proposed method with state-of-the-art models for bias-aware sentiment analysis, including counterfactual data augmentation, adversarial training, Bias-Product-of-Experts (BPoE), Bias-Regularized Neural Network, and Bias-Finder. The evaluation metrics included accuracy, precision, recall, and F1-score. The proposed method significantly outperformed all other models, achieving an accuracy of 92.5% compared to 82.5% from the next best model, the bias-regulated neural network. Furthermore, Bias-BERT demonstrated superior precision (93.2%) and recall (92.1%), indicating its ability to make accurate predictions and capture a large proportion of relevant instances while minimizing false positives and negatives. The F1-score of the proposed method (92.6%) further highlighted its balanced performance between precision and recall, outperforming the next highest F1 score of 79.6% from the bias-regulated neural network. Other models, such as counterfactual data augmentation, adversarial training, and BPoE, exhibited lower performance across all metrics, with F1-scores ranging from 77.8% to 79.1%. These results clearly demonstrated the effectiveness of the proposed method in not only addressing bias in sentiment analysis, but also achieving superior overall classification performance, making it a robust and reliable solution for sentiment prediction tasks. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multicolumn{5}{c}{**Baseline Models**} \\ \hline Logistic Regression & 0.765 & 0.734 & 0.698 & 0.716 \\ SVM & 0.778 & 0.751 & 0.719 & 0.735 \\ MLP & 0.793 & 0.762 & 0.748 & 0.755 \\ LSTM & 0.810 & 0.781 & 0.769 & 0.775 \\ RoBERTa & 0.850 & 0.836 & 0.812 & 0.824 \\ DistilBERT & 0.845 & 0.825 & 0.800 & 0.812 \\ \hline \multicolumn{5}{c}{**Existing Models in the Literature**} \\ \hline Counterfactual data augmentation & 0.805 & 0.784 & 0.771 & 0.778 \\ Adversarial training & 0.820 & 0.798 & 0.783 & 0.791 \\ Bias-Product-of-Experts (BPoE) & 0.815 & 0.792 & 0.779 & 0.786 \\ Bias-Regularized Neural Network & 0.825 & 0.803 & 0.788 & 0.796 \\ BiasFinder & 0.850 & 0.833 & 0.808 & 0.819 \\ \hline
**Proposed method** & **0.925** & **0.932** & **0.921** & **0.926** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the proposed method with baseline models and existing models in the literature on the test set. (Bold represent performance of proposed method). ### Confusion Matrix

The confusion matrix provides a comprehensive evaluation of classification performance, capturing true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). Figure 2 illustrates the confusion matrices for the proposed method, baseline models (MLP, LSTM), and existing approaches such as Bias-Product-of-Experts (BPoE), Bias-Regularized Neural Network, and adversarial training.