Preprocessing Large-Scale Conversational Datasets: A Framework and Its Application to Behavioral Health Transcripts

This study's hybrid approach effectively characterizes errors, evaluates content, and distinguishes text types within unstructured conversational dataset. It provides a foundation for research on conversational data, key methods, and practical guidelines that serve as crucial first steps in ensuring data quality and usability, particularly in the context of mental health sessions. We highlight the importance of integrating clinical experts with artificial intelligence tools while prioritizing data security throughout the process.