The sexual exploitation of minors is a known and persistent problem for law enforcement. Assistance in prioritizing cases of sexual exploitation of potentially risky conversations is crucial. While attempts to automatically triage conversations for the risk of sexual exploitation of minors have been attempted in the past, most computational models use features which are not representative of the grooming process that is used by investigators. Accurately annotating an offender corpus for use with machine learning algorithms is difficult because the stages of the grooming process feed into one another and are non-linear. In this paper we propose a method for labeling risk, tied to stages and themes of the grooming process, using fuzzy sets. We develop a neural network model that uses these fuzzy membership functions of each line in a chat as input and predict the risk of interaction.