We present an analysis of partial automation of content analysis using machine learning methods. We use a decision-tree induction system to learn from manually categorized negotiation transcripts of electronic buyer–seller negotiations. The data we use were gathered using the Web-based negotiation support systems Inspire and SimpleNS. We experiment with various ways of representing the data to find the solution that gives the best results. The experiments show that we can identify, in relatively small data sets, linguistic features of interest for the detection of negotiation behaviour and negotiation-specific topics.