A Primer on Word Embeddings: AI Techniques for Text Analysis in Social Work
It addresses the problem of analyzing text data in social work research, such as case notes and policy documents, but is incremental as it primarily explains existing methods to a new domain.
This paper introduces word embeddings as a transformative technology for analyzing text data in social work research, demonstrating their application through examples like analyzing case notes for housing instability patterns and comparing licensing exams across languages to enhance research workflows.
Word embeddings represent a transformative technology for analyzing text data in social work research, offering sophisticated tools for understanding case notes, policy documents, research literature, and other text-based materials. This methodological paper introduces word embeddings to social work researchers, explaining how these mathematical representations capture meaning and relationships in text data more effectively than traditional keyword-based approaches. We discuss fundamental concepts, technical foundations, and practical applications, including semantic search, clustering, and retrieval augmented generation. The paper demonstrates how embeddings can enhance research workflows through concrete examples from social work practice, such as analyzing case notes for housing instability patterns and comparing social work licensing examinations across languages. While highlighting the potential of embeddings for advancing social work research, we acknowledge limitations including information loss, training data constraints, and potential biases. We conclude that successfully implementing embedding technologies in social work requires developing domain-specific models, creating accessible tools, and establishing best practices aligned with social work's ethical principles. This integration can enhance our ability to analyze complex patterns in text data while supporting more effective services and interventions.