Proceedings of the International Conference on Enterprise and Industrial Systems (ICOEINS 2023)

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Enhancing Fintech P2P Lending Analysis: Integrating LSTM Algorithm and SERVQUAL Model for Aspect-Based Sentiment Analysis

Authors
Bagus Tri Atmaja1, *, Muhardi Saputra2, Faqih Hamami3
1Telkom University, Bandung, Indonesia
2Telkom University, Bandung, Indonesia
3Telkom University, Bandung, Indonesia
*Corresponding author. Email: bagustriatm@student.telkomuniversity.ac.id
Corresponding Author
Bagus Tri Atmaja
Available Online 30 December 2023.
DOI
10.2991/978-94-6463-340-5_6How to use a DOI?
Keywords
Fintech; P2P Lending; SERVQUAL; LSTM
Abstract

This research aims to aspect-based sentiment analysis based on the customer satisfaction theory SERVQUAL model of the Fintech P2P Lending application on the Google Play Store. The massive technological developments in digital money lending are not supported by optimal service and guaranteed data security. The poor service to customers has caused many complaints and bad reviews for the application. Therefore, a method is needed that can measure how good the services of digital fund-offering service providers are. The SERVQUAL model allows companies to measure the performance of their services from an internal and external perspective of the company. This research uses 1000 review data that is given by users that are labeled based on the 5 aspects of the SERVQUAL model. Then it is processed to obtain a machine learning model that can classify whether a review contains SERVQUAL aspects. The data that has been obtained is going to be lemmatized to get clean data in the form of essential words for preprocessing. The algorithm used is Long-short Term Memory (LSTM) which can study the full context of a review. The result is the highest accuracy obtained is 79%.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Enterprise and Industrial Systems (ICOEINS 2023)
Series
Advances in Economics, Business and Management Research
Publication Date
30 December 2023
ISBN
978-94-6463-340-5
ISSN
2352-5428
DOI
10.2991/978-94-6463-340-5_6How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Bagus Tri Atmaja
AU  - Muhardi Saputra
AU  - Faqih Hamami
PY  - 2023
DA  - 2023/12/30
TI  - Enhancing Fintech P2P Lending Analysis: Integrating LSTM Algorithm and SERVQUAL Model for Aspect-Based Sentiment Analysis
BT  - Proceedings of the International Conference on Enterprise and Industrial Systems (ICOEINS 2023)
PB  - Atlantis Press
SP  - 56
EP  - 66
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-340-5_6
DO  - 10.2991/978-94-6463-340-5_6
ID  - Atmaja2023
ER  -