# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 3 Proposed Methodology
### Data Preprocessing
This approach ensures feasibility while providing meaningful real-world insights into the model's performance and fairness. The unified loss function is defined as:

\[L=\mathcal{L}_{CE}(y,f_{db}(x))+\lambda L_{bias}+\mu\sum_{i=1}^{N}f_{i} \tag{4}\]

where \(\mathcal{L}_{CE}(y,f_{db}(x))\) is the cross-entropy loss for sentiment classification. \(L_{bias}\) is the bias-aware loss term that penalizes deviations from fairness, computed using Kullback-Leibler (KL) divergence. \(\sum_{i=1}^{N}f_{i}\) aggregates user feedback \(f_{i}\) over \(N\) feedback instances. \(\lambda\) and \(\mu\) are hyperparameters that control the trade-offs between accuracy, bias mitigation, and feedback integration. #### 3.5.1 User Feedback Process

Feedback was collected selectively from a curated subset of predictions rather than the entire training dataset to ensure scalability and practicality. Specifically, we sampled 10,000 comments from the test set and engaged a diverse group of users, including domain experts, researchers, and general users. This diverse group of participants was carefully chosen to provide diverse perspectives and reduce the risk of introducing new biases. Types of Feedback Collected

1. Accuracy feedback: Users indicated whether the sentiment prediction was correct or incorrect. 2. Bias feedback: Users identified any perceived biases in the predictions, specifying their nature (e.g., racial, gender, cultural) and optionally providing contextual comments for further refinement. #### 3.5.2 Incorporating Feedback into Training

The feedback signals \(f_{i}\) were systematically aggregated to inform model adjustments. The \(L_{feedback}\) term in the unified loss function accounted for user feedback and was expressed as:

\[L_{feedback}=\sum_{i=1}^{N}f_{i}, \tag{5}\]

where \(f_{i}\) is a numeric representation of the feedback provided for the \(i\)th prediction. Feedback was categorized into actionable patterns, and model parameters were adjusted accordingly during training. Feedback Integration Steps

1. Feedback was aggregated and categorized to identify recurring issues or biases. 2. The bias-aware loss function \(L_{bias}\) was updated to incorporate these patterns, penalizing frequently reported biases more heavily. 3. The model parameters were iteratively updated to minimize the unified loss function \(L\), ensuring continuous improvement. 4. Retraining cycles were conducted periodically with feedback-adjusted parameters, and performance was re-evaluated using standard and fairness-specific metrics. This feedback-driven training mechanism allowed the model to dynamically adapt to real-world contexts, enhancing both accuracy and fairness. By focusing on a manageable subset of predictions and engaging a diverse group of users, the feedback loop balanced practicality and impact while avoiding the need for exhaustive labeling of the entire dataset. ### Training Process and Implementation

The training process began with feature extraction, where the input text was tokenized and passed through the BERT model to extract contextual embeddings. The extracted features were used to predict sentiment labels. The loss function, combining cross-entropy loss, KL divergence, and user feedback penalties, was then computed. During training, model parameters were iteratively updated to minimize total loss \(L\), ensuring a balance between precision and fairness. The evaluation of the model involved standard metrics such as accuracy, precision, recall, and F1-score, along with fairness-specific metrics to validate bias reduction. ## 4 Experimental Results and Discussion

### Evaluation Metrics

In this research, we evaluated the performance of our proposed methodology using the following standard metrics:

#### 4.1.1 Accuracy

The proportion of correctly classified instances out of the total instances. \[\text{Accuracy}=\frac{\text{Number of correct predictions}}{\text{Total number of predictions}} \tag{6}\]

#### 4.1.2 Precision

The proportion of true positive predictions out of the total positive predictions made by the model.