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Burnout and job stress are tragic events that unfortunately occur in many professions. In the teaching profession, however, it affects not just the individual, but also several concomitant parties: students, school, and parents. This has lead to the widespread problem of teacher attrition, where the challenge has become not so much to attract teachers, but to retain them. The present research is based on the reflective writing of early career teachers (ECTs). These ECTs volunteered to write short weekly reflections during a period of about half a year. Spotting potential wellbeing problems in these series of reflections, however, calls for careful reading and studying of such large amounts of texts that manual processing became impracticable. Hence, we developed an algorithm which transforms such a stream of reflections into a 3-D visualization of mood changes, in which times of stress and potential for burnout can be detected more easily. This in turns makes it possible to notice points of concern when there is still time to intervene.