Abstract:
This research addresses the inefficiencies in analysing open-ended responses collected via
platforms like Google Forms. It's easy to see patterns in structured data, but you still have to
do it by hand for qualitative textual responses. More and more businesses are using feedback
forms and surveys, but the lack of automation in processing text data makes it harder to get
timely information and make decisions. ClusterForm is a new web-based system that uses
Natural Language Processing (NLP) and the K-Means clustering algorithm to sort open-ended
text responses from Google Sheets. The system uses TF-IDF vectorisation for semantic
analysis, which makes it easier to group similar responses. Python was used to make the
backend services, and JavaScript was used to make the frontend interface easy to use. This lets
users enter Google Sheet links and get clustered outputs. We used clustering performance
metrics like the Silhouette Score and the Davies-Bouldin Index to test the system. The results
showed that the suggested solution greatly sped up processing and improved clustering
accuracy, making it a useful tool for businesses to use to automate the analysis of qualitative
data.
