Automatic Complaints Categorization Using Random Forest and Gradient Boosting - Anwar, Muchamad Taufiq; Pratiwi, Anggy Eka; Udhayana, Khadijah Febriana Rukhmanti (Universitas PGRI Semarang, 2021)
Capturing and responding to complaints from the public is an important effort to develop a good city/country. This project aims to utilize Data Mining to automatize complaints categorization. More than 35,000 complaints in Bangalore city, India, were retrieved from the â€œI Change My Cityâ€ website (https://www.ichangemycity.com). The vector space of the complaints was created using Term Frequencyâ€“Inverse Document Frequency (TF-IDF) and the multi-class text classifications were done using Random Forest (RF) and Gradient Boosting (GB). Results showed that both RF and GB have similar performance with an accuracy of 73% on the 10-classes multi-class classification task. Result also showed that the model is highly dependent on the word usage in the complaint's description. Future research directions to increase task performance are also suggested.