The use of multiple measures for the operationalization of subjective well-being (SWB) is highly recommended, as each approach brings exposure to different errors. To supplement the approaches proposed in the literature, the paper at hand outlines a complemental text mining technique -- sentiment detection -- which presents an efficient solution for big data analyses. Empirical findings from its application to 466 semi-structured life satisfaction (LS) interviews are contrasted with happiness and satisfaction ratings derived from the interviewees themselves as well as from external raters. In addition, detailed insights are presented into the use of emotive language in LS interviews and into relations between base emotions for the following affective features: anger, joy, sadness, disgust, surprise, trust, fear, and anticipation.