지적과 국토정보 (Journal of Cadastre & Land InformatiX)

  • 제49권1호
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  • Pages.65-84
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  • 2019
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  • 2508-3384(pISSN)
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  • 2508-3392(eISSN)
한국국토정보공사 공간정보연구원 (LX Spatial Information Research Institute)
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기계학습 알고리즘을 이용한 주택 모기지 금리에 대한 시민들의 감정예측

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

  • Kim, Yun-Ki (Department of Land Management, Choengju University)
  • 투고 : 2019.04.16
  • 심사 : 2019.06.18
  • 발행 : 2019.06.30
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초록

본 연구의 목적은 기계학습 알고리즘을 이용하여 주택모기지 금리에 대한 시민들의 감정을 예측하는 것이었다. 연구목적을 달성하기 위해 본 연구는 관련문헌을 검토한 다음 두개의 연구 질문을 설정하였다. 또한 연구 질문에 대한 답을 구하기 위해 본 연구는 Akman의 분류에 따라 감정을 분류 한 다음 여섯 가지 기계학습 알고리즘을 이용하여 모기지 금리에 대한 시민들의 감정을 예측하였다. 분석결과 AdaBoost가 모든 평가범주에서 가장 우수한 분류기로 확인되었다. 그러나 Naive Bayes의 성능수준은 다른 분류기들의 성능수준보다 낮은 것으로 밝혀졌다. 또한 본 연구는 어느 분류기가 각 감정범주를 잘 예측해주는지를 파악하기 위해 ROC 분석을 실시하였다. 분석결과, AdaBoost가 모든 감정범주에서 주택모기지 금리에 대한 주민들의 감정을 가장 잘 예측해주는 것으로 확인되었다. 그러나 슬픔범주에서 여섯 가지 알고리즘의 성능수준은 다른 감정범주보다 훨씬 낮게 나타났다.

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.

키워드

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Figure 1. ROC curves for anger emotion class

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Figure 2. ROC curves for disgust emotion class

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Figure 3. ROC curves for fear emotion class

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Figure 4. ROC curves for joy emotion class

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Figure 5. ROC curves for sadness emotion class

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Figure 6. ROC curves for surprise emotion class

Table 1. Performance Evaluation Results

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Table 2. Confusion Matrix for Adaboost

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Table 3. Confusion Matrix for Random Forest

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Table 4. Confusion Matrix for Decision

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Table 5. Confusin Matrix for KNN

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Table 6. Confusion Matrix for Logistic Regression

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Table 7. Confusion Matrix for Naive Bayes

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