# A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
## 3 Proposed Methodology
### Data Preprocessing
The proposed method achieved the highest classification accuracy with 45,300 TP, 47,262 TN, and the lowest FP (1800) and FN (2500), showcasing its robustness in sentiment classification. In contrast, the MLP model recorded higher FP (2400) and FN (3000), reflecting its limitations in handling complex data. The LSTM model showed better performance with reduced FP (2100) and FN (2700), leveraging sequential information effectively. Among existing methods, the Bias-Regularized Neural Network exhibited competitive performance with 2100 FP and 2800 FN, outperforming BPoE, which had higher FP (3100) and FN (2900). Adversarial training achieved a balance with FP at 2600 and FN at 2700 but remained less effective than the proposed approach. ### Bias Evaluation

In this section, we take a closer look at how well our model performed across different demographic groups from the Jigsaw data set. The main goal here was to understand how effectively the model mitigated bias when dealing with diverse categories of people. The data set was divided into several key demographic groups: race, ethnicity, sex, sexual orientation, religion, and disability. Breaking down the data in this way helped us ensure that our model performed fairly and consistently across various groups, which is essential for bias-aware sentiment analysis. * Race and ethnicity: This group included categories such as race (e.g. Asian, Black, White) and ethnicity (e.g., Latino, Hispanic, and other cultural or national groups). It is important to note that **race** generally refers to physical characteristics, such as skin color and facial features, while **ethnicity** refers to broader cultural factors like language, nationality, and traditions. For example, **Asian** is a broad racial category that includes various **ethnicities** such as Chinese, Indian, Japanese, and Korean, each

Figure 2: Confusion matrices for the proposed method and other models. with its own distinctive cultural identity. Similarly, **Latino** refers to an ethnicity that encompasses individuals from various racial backgrounds. * Gender: this included male, female, transgender, and other gender identities. * Sexual orientation: here, we included heterosexual, homosexual (gay or lesbian), bisexual, and other orientations. * Religion: the religious groups included Christian, Jewish, Muslim, Hindu, Buddhist, atheist, and others. * Disability: this group covered physical disabilities, intellectual or learning disabilities, psychiatric or mental illness, and other disabilities. The demographic distribution of the Jigsaw Unexpected Bias in Toxicity Classification dataset, presented in Table 4, includes various categories such as race, ethnicity, sex, sexual orientation, religion, and disability, highlighting the diversity of the data used for bias evaluation. For each of these groups, we evaluated the performance of the model using precision, recall, and F1-score as the key evaluation metrics. Precision told us how many of the model's positive classifications were correct, recall measured how well the model identified all relevant positive cases, and the F1-score provided a balance between the two. What we were really interested in was how our model performed compared to the baseline models and other existing approaches. These comparisons allowed us to see how effective our method was in reducing bias across different demographic groups. It was important that