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http://dx.doi.org/10.22640/lxsiri.2019.49.1.65

Prediction of Citizens' Emotions on Home Mortgage Rates Using Machine Learning Algorithms  

Kim, Yun-Ki (Department of Land Management, Choengju University)
Publication Information
Journal of Cadastre & Land InformatiX / v.49, no.1, 2019 , pp. 65-84 More about this Journal
Abstract
This study attempted to predict citizens' emotions regarding mortgage rates using machine learning algorithms. To accomplish the research purpose, I reviewed the related literature and then set up two research questions. To find the answers to the research questions, I classified emotions according to Akman's classification and then predicted citizens' emotions on mortgage rates using six machine learning algorithms. The results showed that AdaBoost was the best classifier in all evaluation categories. However, the performance level of Naive Bayes was found to be lower than those of other classifiers. Also, this study conducted a ROC analysis to identify which classifier predicts each emotion category well. The results demonstrated that AdaBoost was the best predictor of the residents' emotions on home mortgage rates in all emotion categories. However, in the sadness class, the performance levels of the six algorithms used in this study were much lower than those in the other emotion categories.
Keywords
Machine Learning; Algorithm; Mortgage Rates; Akman; Emotion Classification; AdaBoost; Classifier; Naive Bayes; Performance Level;
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