Patterns and Behaviors during the Coronavirus Disease 2019 Pandemic in Germany: A Natural Language Processing Application

Introduction: This study aimed to identify the underlying patterns and behaviors during the Coronavirus Disease pandemic for future preparedness and response strategies. Methods: We applied natural language processing (NLP) techniques to interview data of qualitative nature collected from 40 German participants across various phases of the study. We then preprocessed the data well, getting rid of stop words, tokenizing, stemming, and lemmatizing the text, all done to ensure that the analysis would be meaningful and accurate. Results: Significant terms from the term frequency-inverse document frequency (TD-IDF) analysis included noting the terms people, mask, vaccination, and vaccinated. Latent semantic analysis expressed major topics in phase I including discussions of experiences, vaccination, government, preventive measures, and public sentiment. Phase II consisted of vaccination efforts, government trust, and public coronavirus opinions, whereas phase III encompassed long-term impacts, trust in preventive measures, and changes in vaccination efforts. Sentiment analysis showed that negative sentiments are more (> 60%). Discussion: The analysis showed that public concerns moved from compliance to skepticism and identified central themes, including vaccination, trust, and emotional burden. TF-IDF and LSA shed light on an evolving discourse in the pandemic, and sentiment analysis showed a pervasive distress. Such insights reinforce the importance of effective communication and mental health interventions during public health emergencies. Conclusions: These findings help us to know more about the pandemic's impact a decade later that may inform future research, public health strategies, and policymaking.