Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Muchamad Taufiq Anwar; Anggy Eka Pratiwi; Khadijah Febriana Rukhmanti Udhayana

doi:10.26877/asset.v3i1.8460

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Muchamad Taufiq Anwar, Anggy Eka Pratiwi, Khadijah Febriana Rukhmanti Udhayana

Abstract

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 “I Change My City” website (https://www.ichangemycity.com). The vector space of the complaints was created using Term Frequency–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.

Keywords

Automatic complaints categorization; Multi-class classification; Data mining; Random forest; Gradient boosting

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DOI: https://doi.org/10.26877/asset.v3i1.8460

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Advance Sustainable Science, Engineering and Technology (ASSET)

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