The data used for this visualization was the collected 120 qualitative responses collected from National University students on how to further improve the services, food quality, and environment using the Hapag-Kainan, a customized version of the Malasakit toolkit. Data cleaning and filtering methods were performed like the removal of stop words, symbols, numbers, and conversion to lowercase was observed. The data was then preprocessed to count its term frequency and inverse document frequency to gather important feature words. The qualitative responses were experimentally analyzed with different values of k clusters. Silhouette coefficient, an intrinsic evaluation metric for calculating clusters, was used to test the separation between two clusters. The visualizations below shows the clustered 120 Hapag-Kainan responses using Tableau software for the scatter plot and WordItOut for word cloud visualization.