Decoding social group representation in American literature using contextualized embedding analysis and bias detection algorithms

American literature has long served as a mirror, reflecting the diverse cultural, social, and political landscapes of the United States. This research investigates the representation of social groups in American literature by employing advanced natural language processing techniques. Specifically, it utilizes contextualized word embedding models to analyze how characters from diverse social identities, particularly in terms of gender, race, and class, are portrayed across a curated corpus of canonical and contemporary American literary texts. The dataset is compiled and preprocessed through tokenization and normalization to prepare the texts for contextual embedding extraction and bias analysis. Bias detection is conducted using a Bidirectional Encoder Representations mutated Weighted Support Vector Machine (BERWSVM) model designed to classify complex social representations. The Contextualized Embedding Association Test (CEAT) isemployed to statistically evaluate the strength of association between social groups and character traits by computing cosine distances between contextual embeddings. Bidirectional Encoder Representations from Transformers (BERT) are used to extract rich semantic representations from the texts, capturing character descriptions, group identity references, and associated traits. The WSVM component classified intersectional group embeddings, enabling the assessment of representational patterns that extend beyond single-identity categorizations. Implemented in Python, the findings show that the BERWSVM approach performs better than multimodal baseline architectures, achieving superior results, with accuracy, F1-score, recall, and precision ranging from 90% to 95%. The findings reveal that the BERWSVM achieved high accuracy in distinguishing characters belonging to intersectional groups, significantly outperforming traditional baseline models. It shows the effectiveness of integrating computational bias detection algorithms with literary interpretation in analyzing social ideologies, representation, diversity, and fairness in narrative structures.