Law firms and law departments periodically conduct surveys of their members or clients to learn about a topic, e.g., engagement, work-from-home policies, client satisfaction, or use of AI software. Questions on the survey may invite respondents to write as much as they want for an answer. For example, “How have you encountered and dealt with supply-chain obstructions?” I will call them “text questions.”

The old-fashioned way to identify and classify ideas from free-text responses to text questions has been to read and code them by hand. The difficulties of that process is why you can benefit from natural language processing (NLP) tools. Defensible and reproducible coding of text is hard to do well because the coder may be biased or inattentive, the codes keep evolving, concepts are entangled, the amount to process is large, or it is monotonous, sucks up time and has no definitive stopping point. In short, coding text by hand is rife with challenges.