Although they help to build engineers, the mentoring programs hardly are reviewed num-ber-wisdom, based on what learners say. Through that gap comes this work - based upon the voices of 3,362 pupils stretched over a ten year span of disciplines, year by year of study. Only by filtering comments with the help of such tools as sentiment decoding and group-finding algorithms, patterns could be observed. There were six distinct filaments that emerged: proj-ect assistance and grants advice, on-campus assistance and career guidance, personal develop-ment and assistance with research issues. Where opinions were floating freely, numbers took form. The score of 0.7985 on the silhouette score was a confirmation of the optimal number of clusters, which was also supported by the measures used, such as WCSS, Calinski-Harabasz, and Davies-Bouldin. Positive sentiment constituted almost 84.6 percent of the answers - con-firming that mentorship helps in the development of learners. This is a good look at numbers to get a clear understanding of how students are going through these programs, which points to practical steps schools can implement. Rather than basing the analysis on intuition, the use of detailed analysis introduces new approaches to evaluate mentoring arrangements, closing gaps left unanswered in previous studies and informing future changes with evidence.