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

Bagus Tri Atmaja (), Muhardi Saputra and Faqih Hamami
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Bagus Tri Atmaja: Telkom University
Muhardi Saputra: Telkom University
Faqih Hamami: Telkom University

A chapter in Proceedings of the International Conference on Enterprise and Industrial Systems (ICOEINS 2023), 2023, pp 56-66 from Springer

Abstract: 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%.

Keywords: Fintech; P2P Lending; SERVQUAL; LSTM (search for similar items in EconPapers)
Date: 2023
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DOI: 10.2991/978-94-6463-340-5_6

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