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Résumé : Abstract Orcas ( Orcinus orca ) are a charismatic species with multiple connections to local economies and culture in the Pacific Northwest. As such, emotions play a role in public perspectives and support for their conservation. This research presents the use of Natural Language Processing (NLP) emotion analysis to identify the basic emotions associated with written public responses to orca conservation proposals. We operationalized a multi‐dimensional emotion lexicon using Python 3 programing to extract the frequency and density of each basic emotion in over 17,000 public comments specific to orca conservation submitted to the Washington state governor's office. We found that respondents held a mixture of emotions. Trust was most often associated with the belief that urgent actions by state government could help orca populations, joy was most associated with statements reflecting how healthy orcas are icons for healthy ecosystems upon which all depend, and sadness was associated with statements lamenting the death of individual orcas and population extinction. These results can help conservation managers understand why people might support or reject conservation actions and move past conservation social science that has prioritized the emotional domain of fear. Moreover, NLP provides descriptive emotion analyses of big text datasets within specific conservation contexts that can be further explored through qualitative analyses.