# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 3 Proposed Methodology
### Data Preprocessing
Notably, among the bias-aware models, the Bias-Regularized Neural Network remained the top performer with an F1-score of 79.6% but still failed to match the proposed method. RoBERTa, DistilBERT, and BiasFinder maintained competitive performance with F1 scores ranging from 92.4% to 92.9%. These results emphasize the robustness and fairness of the proposed method, demonstrating its superior ability to perform unbiased sentiment analysis across diverse gender groups. #### 4.7.3 Sexual Orientation

Table 16 presents the results of the bias evaluation for different groups of sexual orientation using precision, recall, and F1-score. Across all groups--heterosexual, homosexual, and others--Bias-BERT consistently outperformed baseline models and existing bias-aware methods. For the heterosexual group, Bias-BERT achieved 93.3% precision, 93.9% recall, and a 93.6% F1-score, far surpassing Logistic Regression (F1-score of 71.6%), SVM (73.4%), MLP (75.7%), and LSTM (77.5%). Among bias-aware models, BiasFinder performed the best with a 92.2% F1 score, followed by the Bias-Regularized Neural Network with a 79.7% F1-score, but both still lagged behind Bias-BERT. A similar pattern was observed for the homosexual and bisexual groups, where Bias-BERT maintained its dominance with F1 scores of 93.3% and 93.4%, respectively. BiasFinder achieved F1 scores of 92.3% and 92.4% for these groups, while Bias-Regularized Neural Network achieved an F1-score of 79.7%, but both still fell short of Bias-BERT. The 'other sexual orientation' showed the same trend, with Bias-BERT delivering an F1-score of 93.5%, significantly ahead

of other models. The proposed Bias-BERT demonstrated superior performance across all sexual orientation groups, highlighting its robustness and fairness in addressing biases and ensuring a balanced sentiment analysis across diverse groups. #### 4.7.4 Religion

Table 14 presents the results of the bias evaluation for various religious groups, comparing the performance of the proposed Bias-BERT model with the baseline and existing bias-aware models using precision, recall, and F1-score. For all religious groups--Christian, Jewish, Muslim, Hindu, Buddhist, atheist, and others--Bias-BERT consistently outperformed other models. For example, in the Christian group, Bias-BERT achieved 93.0% precision, 93.6% recall, and a 93.3% F1-score, far exceeding Logistic Regression (F1 score of 71.6%) and advanced baseline models such as LSTM (77.5% F1 score). Similarly, among bias-aware models, Bias-Regularized Neural Network performed best with an F1 score of 79.6% but still fell short of Bias-BERT. This trend held across all groups, with Bias-BERT maintaining superior performance, such as an F1 score of 93.5% for the Jewish group, 93.6% for the Muslim group, 93.4% for the Hindu group, and 93.3% for the Buddhist group. Even for groups with potentially underrepresented data, such as atheists and others, Bias-BERT excelled with F1 scores of 93.5% and 93.6%, respectively, outperforming both baseline and bias-aware models. Additionally, RoBERTa, DistilBERT, and BiasFinder showed strong performance, but still lagged behind Bias-BERT, with F1-scores ranging from 92.2% to 92.4% across different groups. In this analysis, our proposed Bias-BERT demonstrated remarkable robustness and fairness, achieving consistent and superior results across all religious groups, thus validating its effectiveness in delivering an accurate and unbiased sentiment analysis. #### 4.7.5 Disability

Table 15 summarizes the performance of Bias-BERT, baseline models, and existing bias-aware models across different disability groups using precision, recall, and F1-score. Bias-BERT consistently outperformed all other models across all groups, demonstrating its superior ability to provide accurate and unbiased sentiment analysis. For the physical disability group, Bias-BERT achieved 93.1% precision, 93.7% recall, and a 93.4% F1-score, far surpassing Logistic Regression (F1-score of 71.6%) and advanced baseline models like LSTM (F1-score of 77.5%).