Smell disorders are commonly reported with COVID-19 infection. Some patients show prolonged smell-related issues, even after the respiratory symptoms are resolved. To explore the concerns of patients, and to provide an overview for each specific smell disorder, we explored existing data from a longitudinal survey, and contained self-reports on the changes of smell that participants experienced at two time points. People who still suffered from smell disorders at the second time point, hence named ‘longhaulers’, were compared to those who were not, hence named ‘non-longhaulers’. Specifically, three aims were pursued in this study. First, to classify smell disorders based on the participants’ self-reports. Second, to classify the sentiment of each self-report using a machine learning approach, and third, to find specific keywords that best describe the smell dysfunction in those self-reports. We found that the prevalence of parosmia and hyposmia was higher in longhaulers than in non-longhaulers. Furthermore, the results suggest that longhaulers stated self-reports with more negative sentiment than non-longhaulers. Finally, we found specific keywords that were more typical for either longhaulers compared to non-longhaulers. Taken together, our work shows consistent findings with previous studies, while at the same time, provides new insights for future studies investigating smell disorders.