The Emerging Role of Big Data and Machine Learning Technologies in Credit Scoring

With the rapid development of big data and machine learning technologies, the application of sentiment analysis in financial credit scoring is gradually revolutionizing the traditional risk assessment paradigm. Traditional credit scoring models rely on structured financial data, making it difficult to capture the dynamic sentimental signals of borrowers and the potential impact of market sentiment. In this paper, this paper systematically explore the practical path of sentiment analysis technology to build a multi-dimensional credit risk portrait by mining unstructured data such as social media comments, news texts, customer feedback, etc., combined with machine learning algorithms (e.g., BERT, XGBoost, LSTM). Studies have shown that sentiment features can significantly improve the predictive ability of credit scoring models: in terms of technology integration, big data infrastructure (e.g., Hadoop, Spark) supports the real-time processing of massive text, while deep learning models (e.g., BLIP-NLP) realize the fine-grained extraction of sentiment signals. However, sentiment analysis in the financial domain still faces challenges such as data noise, dynamic adaptation, model interpretability, and privacy ethics.