Aspect-Based Sentiment Analysis on Application Review using CNN

Arta Aritonang, Putri and Evelin Johan, Monika and Prasetiawan, Iwan (2022) Aspect-Based Sentiment Analysis on Application Review using CNN. Aspect-Based Sentiment Analysis on Application Review using CNN, 13 (1). pp. 54-61. ISSN e-ISSN 2549-4015 print ISSN 2085-4579

Text Aspect-Based_Sentiment_Analysis_on_Application_Review using CNN.pdf Download (695kB)

Abstract

As an obligatory application during the COVID-19 pandemic by Indonesians, PeduliLindungi must have provided outstanding quality services to its users. However, as of December 2021, users’ sentiment toward the quality and service of the PeduliLindungi application was still low, with an application rating of 3.6 out of 5 on the Google Play Store. This study uses text mining techniques for the Aspect-Based Sentiment Analysis (ABSA) task in the PeduliLindungi application review, a sentiment analysis task based on the aspect category of the application. This study aims to classify the users’ sentiment on aspects of the application and provide insight and knowledge to improve the quality of the PeduliLindungi application. The ABSA method used in this study is the classification of aspects and sentiments using the Convolutional Neural Network (CNN) algorithm. The results showed that the CNN model could produce such good performance with an f1 score of 92.23% in the aspect classification and 95.13% in the sentiment classification. The results of user sentiment modelling showed the dominance of negative sentiment in the eight aspects of the application, namely Visual Experience, Scan – Check-in/out, Vaccine Certificate, eHac, COVID Test, Register/Login, Performance and Stability, and Privacy, Data, and Security.

Item Type:	Article
Keywords:	Aspect-Based Sentiment Analysis, Convolution Neural Network, PeduliLindungi, Text Classification, Text Mining
Subjects:	000 Computer Science, Information and General Works 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 001 Knowledge > 001.4 Research
Divisions:	Faculty of Engineering & Informatics > Information System
Depositing User:	Iwan Prasetiawan (L00552)
Date Deposited:	20 Mar 2024 03:55
Last Modified:	20 Mar 2024 03:55
URI:	https://kc.umn.ac.id/id/eprint/29705

Actions (login required)

View Item