This paper delved into analyzing leadership communication within an academic institution by applying Natural Language Processing (NLP) techniques to a dataset of internal memos. The study aimed to uncover the nature of leadership communication, assess sentiment, and identify prevalent topics within these memos. The investigation began by collecting a set of ten memos from the university’s top management. These memos were subjected to preprocessing techniques, including text cleaning, tokenization, stopword removal, part-of-speech (POS) tagging, and lemmatization. Sentiment analysis was employed to classify the memos as objective or subjective, positive or negative. The results showed that the majority of memos were objective and conveyed a positive tone, indicating a professional and constructive leadership style. Negative words were linked to topics such as class suspensions and health breaks. Using Latent Dirichlet Allocation (LDA), topic modeling revealed that the predominant topics in the memos revolved around participation in university activities and guidelines for flexible learning amid the COVID-19 pandemic. This research shed light on the objectivity and professionalism of the University’s leadership. The findings offered useful implications for managers, staff, and policymakers.