Researchers have long known that the words of a text have always contained more information than on the surface. As such, texts have been studied for subtexts and other latent or hidden information. One approach has involved the machine-enabled analysis of human sentiment, usually mapped out on a positive-negative polarity. NVivo 11 Plus (a qualitative research tool released in late 2015) enables the automated sentiment analysis of texts (coded research, formal articles, text corpora, Tweetstream datasets, Facebook wall posts, websites, and other sources) based on four categories: very positive, moderately positive, moderately negative, and very negative. The tool feature compares the target text set against a sentiment dictionary and enables coding at different units of analysis: sentence, paragraph, or cell. Further, the sentiment capability extracts the coded text into respective text sets which may be further analyzed using text frequency counts, text searches, automated theme and sub-theme extractions (topic modeling), and data visualizations.