User Review Automation: Detecting Actionable Complaints on Gojek in the Play Store using the LSTM Method

Indira Nailah Ramadhani, Winda Kurnia Sari, Ken Ditha Tania

Abstract


This study aims to develop an automatic complaint detector for Gojek app reviews using Long Short Term Memory (LSTM). The dataset consists of 225,002 user reviews on the Google Play Store. The purpose of this study itself is to facilitate the service team in understanding the shortcomings of the application complained by users. Automatic complaint detection will facilitate the service team to take action to resolve the problems experienced by users. Therefore, the review data provided by users is properly processed using LSTM to create an effective and efficient detection
system. Processing is carried out using three different data sharing ratios, namely 90:10, 80:20, and 70:30 to ensure that the system is stable and effective. The accuracy results of the three data sharing ratios reached above 90%, thus proving that the system is able to detect complaints well. A pre-built dashboard is used to visualize the results of the system built using LSTM to facilitate monitoring the classification results. This system is expected to facilitate companies in detecting all user complaints and finding solutions to improve services to provide comfort for users.

Keywords


gojek; long short-term memory; complaint prediction; user reviews

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References


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DOI: https://doi.org/10.32520/stmsi.v14i6.5708

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