Currently, residents in Singapore’s public housing estates provide feedback on their living environment through multiple channels such as call centers, emails, and mobile applications. The feedbacks from residents can be in the form of complaint, compliment, suggestion, or service request. In order to facilitate estate management and timely response to the cases, it is fundamental to ensure a proper categorization of the feedbacks. As up to thousands of cases can be received per day, it is not feasible for human operators to manually label the feedbacks.
To alleviate this issue, Surbana Technologies, Institute of Data Science (IDS) and Singapore Data Science Consortium (SDSC) will jointly work together to develop a two-part pilot study:
Part 1: Explore the feasibility of using artificial intelligence (AI) techniques to automatically categorize residents’ textual feedback. Machine learning (ML) and natural language processing (NLP) algorithms will be used to identify complaints from the residents’ feedback.
Part 2: Study the possible relation between lift-related residents’ feedback and actual lift breakdowns detected by the lift monitoring devices.
Project Title: Automatic Categorization of Residents’ Textual Feedback for Complaints and Association of the Textual Feedback with Actual Lift Faults Detected by Telemonitoring
Done By: Surbana Technologies Pte Ltd, Institute of Data Science (IDS) and Singapore Data Science Consortium (SDSC)
Contact Person: Dr. Manoranjan Dash, Senior Data Scientist, SDSC
This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Industry Alignment Fund (Pre-positioning) Funding Initiative.