There is a need for more effective and efficient natural language processing (NLP) solutions. This need can be addressed by, for example, solutions for performing NLP analysis by utilizing joint topic-sentiment (JST) modeling. In one example, a method comprises receiving a per-document topic distribution for a digital document, wherein the per-document topic distribution comprises a per-document topic correlation indication for each candidate topic designation; receiving a per-document topic-sentiment distribution for the digital document, wherein the per-document topic-sentiment distribution comprises a per-document topic-sentiment correlation indication for each topic-sentiment pair of a candidate topic designation and a candidate sentiment designation; generating, based at least in part on the per-document topic distribution and the per-document topic-sentiment distribution, a topic designation and a sentiment designation for each selected word in the digital document; and generating a JST modeling output based a on each topic designation and each sentiment designation.