# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 3 Proposed Methodology
### Data Preprocessing
```
0: Training dataset \(D_{\text{train}}\), test dataset \(D_{\text{test}}\), demographic group \(G\)
0: Trained model \(M\), accuracy, precision, recall, F1-score
1:functionPreprocessData(\(D\))
2:for each comment \(c\) in \(D\)do
3:\(c\leftarrow\text{clean}(c)\)
4:\(c\leftarrow\text{tokenize}(c)\)
5:\(c\leftarrow\text{normalize}(c)\)
6:endfor
7:return\(D\)
8:endfunction
9:functionFilterByGroup(\(D,G\))
10:\(D_{G}\leftarrow[]\)
11:for each comment \(c\) in \(D\)do
12:if\(c.\text{group}=G\)then
13:\(D_{G}.\text{append}(c)\)
14:endif
15:endfor
16:return\(D_{G}\)
17:endfunction
18:functionTrainModel(\(X_{\text{train}},\lambda\))
19: Initialize Bias-BERT model \(M\)
20:\(L_{\text{CE}}\leftarrow\text{define\_cross}.\text{entropy\_loss}()\)
21:\(L_{\text{bias}}\leftarrow\text{define\_KL\_divergence\_loss}()\)
22:\(L\leftarrow\mathcal{L}_{CE}(y,f_{db}(x))+\lambda L_{\text{bias}}+\mu\sum_{i= 1}^{N}f_{i}\)
23:\(M\leftarrow\text{minimize\_loss}(M,X_{\text{train}},L)\)
24:return\(M\)
25:endfunction
26:functionUpdateModel(\(M\), feedback)
27:for each \(f\) in feedback do
28: Update \(M\) based on \(f\)
29:endfor
30:return\(M\)
31:endfunction
32:\(D_{\text{train}}\leftarrow\text{PreprocessData}(D_{\text{train}})\)
33:\(D_{\text{test}}\leftarrow\text{PreprocessData}(D_{\text{test}})\)
34:\(D_{\text{train-G}}\leftarrow\text{FilterByGroup}(D_{\text{train}},G)\)
35:\(D_{\text{test-G}}\leftarrow\text{FilterByGroup}(D_{\text{test}},G)\)
36:\(X_{\text{train}}\leftarrow\text{extract\_features}(D_{\text{train-G}})\)
37:\(X_{\text{test}}\leftarrow\text{extract\_features}(D_{\text{test-G}})\)
38:\(\lambda\gets\text{hyperparameter\_value}\)
39:\(M\leftarrow\text{TrainModel}(X_{\text{train}},\lambda)\)
40:feedback\(\leftarrow\) collect_user_feedback()
41:if feedback \(\
eq\) empty then
42:\(M\leftarrow\text{UpdateModel}(M,\text{feedback})\)
43:\(M\leftarrow\text{TrainModel}(X_{\text{train}},\lambda)\)
44:endif
45:predictions \(\gets M.\text{predict}(X_{\text{test}})\)
46:Evaluate predictions to get accuracy, precision, recall, F1-score

### Bias-Aware Sentiment Classifier

In this section, we introduce Bias-BERT, a sentiment classifier leveraging the power of BERT to perform sentiment analysis while actively mitigating biases in predictions. The classifier integrates a bias-aware loss function to ensure fairness, reduce biases, and enhance the interpretability of its outputs. Let the input text be denoted as \(x\) and its corresponding sentiment label as \(y\). The BERT model learns a mapping function \(f(\cdot)\) from \(x\) to \(y\), i.e., \(y=f(x)\). In Bias-BERT, the goal is to learn a debiased mapping function \(f_{db}(\cdot)\), to ensure accurate sentiment classification while minimizing biases. This is achieved through the bias-aware loss function, defined as:

\[L=\mathcal{L}_{CE}(y,f_{db}(x))+\lambda L_{bias} \tag{2}\]

where \(\mathcal{L}_{CE}\) represents the cross-entropy loss for sentiment classification, \(L_{bias}\) is the bias-aware loss term that penalizes deviations from fairness, and \(\lambda\) is a hyperparameter controlling the trade-off between classification accuracy and bias mitigation. The bias-aware term \(L_{bias}\) is formulated using the Kullback-Leibler (KL) divergence, which measures the divergence between the predicted sentiment distribution \(P(y|x)\) and a reference unbiased distribution \(Q(y)\). The bias-aware term is expressed as:

\[L_{bias}=D_{KL}(P(y|x)\parallel Q(y))=\sum_{y}P(y|x)\log\bigg{(}\frac{P(y|x)} {Q(y)}\bigg{)} \tag{3}\]

where \(P(y|x)\) is the predicted probability distribution of sentiment labels for input \(x\), and \(Q(y)\) is the reference unbiased distribution, typically a uniform distribution where all sentiment classes are equally likely. ### Loss Function with User Feedback Integration

To enhance the model's adaptability and fairness, a user feedback loop is incorporated into the training process, focusing on a representative subset of predictions rather than the entire dataset.