Suicidal rates have been increasing since 2000 according the latest report of Centers for disease control and prevention. Today internet opens a channel where people can communicate and information remains registered, making acoustic, semantic and syntactic analyses especially appealing to find hidden cues that can be used to detect signs of different mental conditions. Here we analyze poems from poets who committed suicide to develop a method to detect suicidal signs. We use bipartite graph matching algorithms after data retrieval to assure our results are not susceptible to bias created by variation in poem sample size among poets, and focus on linguistic content (e.g. similarity to specific concepts) and structure (e.g. density of ideas). Our results using different classifiers yield accuracy rates of up to 86% to discriminate suicidal from non-suicidal poets.