KnE Social Sciences
ISSN: 2518-668X
The latest conference proceedings on humanities, arts and social sciences.
Analysis of Yogyakarta Coffee Shop Visitor Reviews to Increase Customer Satisfaction Using Sentiment Analysis
Published date: Mar 22 2024
Journal Title: KnE Social Sciences
Issue title: International Conference on Engineering Management and Sustainable Innovative Technology (ICEMSIT)
Pages: 30–39
Authors:
Abstract:
Visitor reviews written on Google Reviews can show the quality of a product or business. It can also indirectly be a promotion that attracts new consumers. There is a lot of information that can be processed from Google Review that is useful for improving business quality and customer satisfaction. One method that can be used to analyze review data is sentiment analysis. This study analyzed the reviews of coffee shop visitors in the Yogyakarta area written on Google Rewies using the Naïve Bayes Method. Visitor reviews were analyzed using sentiment analysis to see if visitor reviews tend to be positive or negative. Coffee shop business voters can see the level of customer satisfaction and find out what things need to be maintained and improved to increase customer satisfaction. The results of the sentiment analysis showed that more words were detected as positive than negative. Coffee shop visitors in Yogyakarta showed more positive emotions about their experiences when visiting coffee shops, which means most visitors were satisfied with the services and products offered by coffee shop owners in Yogyakarta. Visitors most often wrote about good coffee, price, friendly, suitable, spacious parking, hanging out, comfort, food, service, taste, and working space. Thus, coffee shop owners should focus on those things to increase their customer satisfaction.
Keywords: visitor reviews, Google Reviews, sentiment analysis, customer satisfaction
References:
[1] Tjiptono F. Prinsip-Prinsip Total Quality Service. Yogyakarta: Andy Offset; 2011.
[2] Ladhari R, Brun I, Morales M. Determinants of dining satisfaction and post-dining behavioral intentions. Int J Hospit Manag. 2008;27(4):563–73.
[3] Han H, Ryu K. The Roles of the Physical Environment, Price Perception, and Customer Satisfaction in Determining Customer Loyalty in the Restaurant Industry. J Hosp Tour Res (Wash DC). 2009;33(4):487–510.
[4] Jiang P, Zhang C, Fu H, Niu Z, Yang Q. An Approach Based on Tree Kernels for Opinion Mining of Online Product Reviews. 2010 IEEE International Conference on Data Mining. IEEE; 2010. pp. 256–265.
[5] Rozi IF, Pramono SH, Dahlan EA. Implementasi Opinion Mining (Analisis Sentimen) untuk Ekstraksi Data Opini Publik pada Perguruan Tinggi. J EECCIS. 2013;6:37–43.
[6] Pasaribu DJM, Kusrini K, Sudarmawan S. Peningkatan Akurasi Klasifikasi Sentimen Ulasan Makanan Amazon dengan Bidirectional LSTM dan Bert Embedding. Inspir J Teknol Inf dan Komun. 2020;10:9–20.
[7] Naquitasia R, Fudholi D, Iswari L. Analisis Sentimen Berbasis Aspek pada Wisata Halal dengan Metode Deep Learning. J Teknoinfo. 2022;16(2):156–64.
[8] Somantri O, Dairoh. Analisis Sentimen Penilaian Tempat Tujuan Wisata Kota Tegal Berbasis Text Mining. J Edukasi dan Penelit Inform. 2019;5:191–196.
[9] Ferryawan R, Kusrini K, Wibowo FW. Analisis Sentimen Wisata Jawa Tengah Menggunakan Na?ve Bayes. J Inf J Penelit dan Pengabdi Masy. 2020;5:55–60.
[10] Chandani V, Wahono R, Purwanto P. Komparasi Algoritma Klasifikasi Machine Learning Dan Feature Selection pada Analisis Sentimen Review Film. J Intell Syst. 2015;1:56–60.
[11] Hidayatullah AF. SN A. Analisis Sentimen dan Klasifikasi Kategori terhadap Tokoh Publik pada Twitter. Seminar Nasional Informatika (semnasIF). Yogyakarta: UPN Veteran; 2014. pp. 115–22.
[12] Nugroho DG, Chrisnanto YH, Wahana A. Analisis Sentimen Pada Jasa Ojek Online Menggunakan Metode Naive Bayes. In: Seminar Nasional Sains dan Teknologi Fakultas Teknik universitas Wahid Hasyim. Semarang; 2016. pp. 156–161.
[13] Gunawan B, Pratiwi HS, Pratama EE. Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes. J Edukasi dan Penelit Inform. 2018;4:113.
[14] Hand DJ. Principles of data mining. Drug Saf. 2007;30(7):621–2.
[15] Hussein DM. A survey on sentiment analysis challenges. J King Saud Univ Eng Sci. 2018;30(4):330–8.
[16] Liu B. Sentiment Analysis and Subjectivity Bing Liu. Handbook of Natural Language Processing. Chapman and Hall/CRC; 2010. pp. 651–90.