Analysis of Tenants Complaints Using Text Mining

Abstract

Tenant complaints are critical for property management companies, and they must be resolved immediately to maintain tenant satisfaction levels. However, keeping a close watch on long and unstructured complaints on any textual records is quite challenging. Hence, this paper aimed to visualise frequent issues raised in the complaints and classify complaints using text mining approaches. Analysis was done on three-month complaint records from a property management company in Shah Alam, Malaysia. The result showed that light is the most frequent word in tenant complaints, followed by water. The high frequency of the two words indicated that recurring issues raised by the tenant are regarding lighting and water problems associated with leaking pipes. In addition, complaints can be classified into four groups: light, sink, door and toilet problems. This study uses a well-established text mining technique to analyse and evaluate the voice of tenants. The information acquired through text mining analysis highlighted current issues that require the property management's attention.