Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default

Katleho Makatjane

doi:doi:10.11648/j.ajdmkd.20220702.11

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Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default

Katleho Makatjane

Published in American Journal of Data Mining and Knowledge Discovery (Volume 7, Issue 2)

Received: 20 July 2022 Accepted: 10 August 2022 Published: 9 January 2023

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Abstract

Social networks have taken the world by storm with their fast and commendable speed. It could be social, political, or present with all sorts of situations that arise. People’s opinions around the globe are articulated through social media, making it apposite for drawing out opinions. Organizations that aim at refining their products and services use sentimental analysis methods to increase their resources. In the banking and financial industry, it is much easier to get feedback from customers through Twitter and or Facebook sentimental analysis. The elements associated with Twitter or consumers and services providers who want to know who they are, and what they are in their daily life towards their bank and financial portfolios cannot suppress Facebook sentimental analysis. Hence, this study aims to predict the probability of bank loan default and classify the Twitter messages by exhibiting the results of deep learning algorithms. High-performance computing with hyper-parameter space for grid-search (HPSGS) and hyper-parameter optimization (HPO) are developed and compared with the effectiveness of three gradient boosting decision trees. The results reveal that the XGboot algorithm has a better prediction or features a score that is better as compared to other algorithms at 91 percent in the test data and 93 percent performance in the validation data. It is also seen that women are more likely to default than men as across all the algorithms, their likelihood of risk or default is higher than that of men. These results are useful for decision-makers and the financial sector for future use and planning in credit risk and bank loan default-prone areas.

Published in	American Journal of Data Mining and Knowledge Discovery (Volume 7, Issue 2)
DOI	10.11648/j.ajdmkd.20220702.11
Page(s)	5-12
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Bank Loan, Credit Risk, Data Mining, Deep Learning, Machine Learning, Sentiment Analysis

References

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APA Style

Katleho Makatjane. (2023). Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default. American Journal of Data Mining and Knowledge Discovery, 7(2), 5-12. https://doi.org/10.11648/j.ajdmkd.20220702.11

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ACS Style

Katleho Makatjane. Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default. Am. J. Data Min. Knowl. Discov. 2023, 7(2), 5-12. doi: 10.11648/j.ajdmkd.20220702.11

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AMA Style

Katleho Makatjane. Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default. Am J Data Min Knowl Discov. 2023;7(2):5-12. doi: 10.11648/j.ajdmkd.20220702.11

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@article{10.11648/j.ajdmkd.20220702.11,
  author = {Katleho Makatjane},
  title = {Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {7},
  number = {2},
  pages = {5-12},
  doi = {10.11648/j.ajdmkd.20220702.11},
  url = {https://doi.org/10.11648/j.ajdmkd.20220702.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20220702.11},
  abstract = {Social networks have taken the world by storm with their fast and commendable speed. It could be social, political, or present with all sorts of situations that arise. People’s opinions around the globe are articulated through social media, making it apposite for drawing out opinions. Organizations that aim at refining their products and services use sentimental analysis methods to increase their resources. In the banking and financial industry, it is much easier to get feedback from customers through Twitter and or Facebook sentimental analysis. The elements associated with Twitter or consumers and services providers who want to know who they are, and what they are in their daily life towards their bank and financial portfolios cannot suppress Facebook sentimental analysis. Hence, this study aims to predict the probability of bank loan default and classify the Twitter messages by exhibiting the results of deep learning algorithms. High-performance computing with hyper-parameter space for grid-search (HPSGS) and hyper-parameter optimization (HPO) are developed and compared with the effectiveness of three gradient boosting decision trees. The results reveal that the XGboot algorithm has a better prediction or features a score that is better as compared to other algorithms at 91 percent in the test data and 93 percent performance in the validation data. It is also seen that women are more likely to default than men as across all the algorithms, their likelihood of risk or default is higher than that of men. These results are useful for decision-makers and the financial sector for future use and planning in credit risk and bank loan default-prone areas.},
 year = {2023}
}

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TY  - JOUR
T1  - Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default
AU  - Katleho Makatjane
Y1  - 2023/01/09
PY  - 2023
N1  - https://doi.org/10.11648/j.ajdmkd.20220702.11
DO  - 10.11648/j.ajdmkd.20220702.11
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 5
EP  - 12
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20220702.11
AB  - Social networks have taken the world by storm with their fast and commendable speed. It could be social, political, or present with all sorts of situations that arise. People’s opinions around the globe are articulated through social media, making it apposite for drawing out opinions. Organizations that aim at refining their products and services use sentimental analysis methods to increase their resources. In the banking and financial industry, it is much easier to get feedback from customers through Twitter and or Facebook sentimental analysis. The elements associated with Twitter or consumers and services providers who want to know who they are, and what they are in their daily life towards their bank and financial portfolios cannot suppress Facebook sentimental analysis. Hence, this study aims to predict the probability of bank loan default and classify the Twitter messages by exhibiting the results of deep learning algorithms. High-performance computing with hyper-parameter space for grid-search (HPSGS) and hyper-parameter optimization (HPO) are developed and compared with the effectiveness of three gradient boosting decision trees. The results reveal that the XGboot algorithm has a better prediction or features a score that is better as compared to other algorithms at 91 percent in the test data and 93 percent performance in the validation data. It is also seen that women are more likely to default than men as across all the algorithms, their likelihood of risk or default is higher than that of men. These results are useful for decision-makers and the financial sector for future use and planning in credit risk and bank loan default-prone areas.
VL  - 7
IS  - 2
ER  -

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Author Information

Katleho Makatjane

Department of Statistics, University of Botswana, Gaborone, Botswana

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Plain Text BibTeX RIS

APA Style

Katleho Makatjane. (2023). Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default. American Journal of Data Mining and Knowledge Discovery, 7(2), 5-12. https://doi.org/10.11648/j.ajdmkd.20220702.11

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ACS Style

Katleho Makatjane. Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default. Am. J. Data Min. Knowl. Discov. 2023, 7(2), 5-12. doi: 10.11648/j.ajdmkd.20220702.11

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AMA Style

Katleho Makatjane. Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default. Am J Data Min Knowl Discov. 2023;7(2):5-12. doi: 10.11648/j.ajdmkd.20220702.11

Copy | Download

@article{10.11648/j.ajdmkd.20220702.11,
  author = {Katleho Makatjane},
  title = {Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default},
  journal = {American Journal of Data Mining and Knowledge Discovery},
  volume = {7},
  number = {2},
  pages = {5-12},
  doi = {10.11648/j.ajdmkd.20220702.11},
  url = {https://doi.org/10.11648/j.ajdmkd.20220702.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20220702.11},
  abstract = {Social networks have taken the world by storm with their fast and commendable speed. It could be social, political, or present with all sorts of situations that arise. People’s opinions around the globe are articulated through social media, making it apposite for drawing out opinions. Organizations that aim at refining their products and services use sentimental analysis methods to increase their resources. In the banking and financial industry, it is much easier to get feedback from customers through Twitter and or Facebook sentimental analysis. The elements associated with Twitter or consumers and services providers who want to know who they are, and what they are in their daily life towards their bank and financial portfolios cannot suppress Facebook sentimental analysis. Hence, this study aims to predict the probability of bank loan default and classify the Twitter messages by exhibiting the results of deep learning algorithms. High-performance computing with hyper-parameter space for grid-search (HPSGS) and hyper-parameter optimization (HPO) are developed and compared with the effectiveness of three gradient boosting decision trees. The results reveal that the XGboot algorithm has a better prediction or features a score that is better as compared to other algorithms at 91 percent in the test data and 93 percent performance in the validation data. It is also seen that women are more likely to default than men as across all the algorithms, their likelihood of risk or default is higher than that of men. These results are useful for decision-makers and the financial sector for future use and planning in credit risk and bank loan default-prone areas.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Deep Learning for Sentiment Analysis to Predict the Probability of Bank Loan Default
AU  - Katleho Makatjane
Y1  - 2023/01/09
PY  - 2023
N1  - https://doi.org/10.11648/j.ajdmkd.20220702.11
DO  - 10.11648/j.ajdmkd.20220702.11
T2  - American Journal of Data Mining and Knowledge Discovery
JF  - American Journal of Data Mining and Knowledge Discovery
JO  - American Journal of Data Mining and Knowledge Discovery
SP  - 5
EP  - 12
PB  - Science Publishing Group
SN  - 2578-7837
UR  - https://doi.org/10.11648/j.ajdmkd.20220702.11
AB  - Social networks have taken the world by storm with their fast and commendable speed. It could be social, political, or present with all sorts of situations that arise. People’s opinions around the globe are articulated through social media, making it apposite for drawing out opinions. Organizations that aim at refining their products and services use sentimental analysis methods to increase their resources. In the banking and financial industry, it is much easier to get feedback from customers through Twitter and or Facebook sentimental analysis. The elements associated with Twitter or consumers and services providers who want to know who they are, and what they are in their daily life towards their bank and financial portfolios cannot suppress Facebook sentimental analysis. Hence, this study aims to predict the probability of bank loan default and classify the Twitter messages by exhibiting the results of deep learning algorithms. High-performance computing with hyper-parameter space for grid-search (HPSGS) and hyper-parameter optimization (HPO) are developed and compared with the effectiveness of three gradient boosting decision trees. The results reveal that the XGboot algorithm has a better prediction or features a score that is better as compared to other algorithms at 91 percent in the test data and 93 percent performance in the validation data. It is also seen that women are more likely to default than men as across all the algorithms, their likelihood of risk or default is higher than that of men. These results are useful for decision-makers and the financial sector for future use and planning in credit risk and bank loan default-prone areas.
VL  - 7
IS  - 2
ER  -

Copy | Download