Sentiment Analysis of Shopee App Reviews Using Random Forest and Support Vector Machine


Suswadi Suswadi(1); Moh. Erkamim(2*);

(1) Universitas Tunas Pembangunan Surakarta
(2) Universitas Tunas Pembangunan Surakarta
(*) Corresponding Author

  

Abstract


During the COVID-19 outbreak, Indonesian marketplaces were significantly impacted including Shopee app. It is necessary to evaluate the features and services of the Shopee application by looking at the feedback given by the public in Google Play Store reviews. This is what prompted research to be conducted from Kaggle data in the form of Shopee reviews. From this data, sentiment analysis is carried out utilizing the Support Vector Machine (SVM) and Random Forest methods. This method are used to classify reviews based on positive and negative sentiments. The results showed that the level of classification accuracy in the Random Forest model is 82.21%. While the SVM model provides a higher level of accuracy of 84.71%. Data exploration on positive and negative sentiment classes is used to find insight into this problem. In positive sentiment, words that often appear such as “belanja”, “aplikasi”, and “barang” are found. As for the negative sentiments, namely “ongkir”, “kirim”, “aplikasi”. These words can be used to be a quality improvement or evaluation for the Shopee company.

Keywords


Covid-19; Random Forest; Sentiment Analysis; Shopee; Support Vector Machine

  
  

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doi  https://doi.org/10.33096/ilkom.v15i3.1610.427-435 		  

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M. I. Purba, D. C. Y. Simanjutak, Y. N. Malau, W. Sholihat, and E. A. Ahmadi, “The effect of digital marketing and e-commerce on financial performance and business sustaina-bility of MSMEs during COVID-19 pandemic in Indonesia,” Int. J. Data Netw. Sci., vol. 5, no. 3, pp. 275–282, 2021, doi: 10.5267/j.ijdns.2021.6.006.

R. Rosita, “Pengaruh Pandemi Covid-19 Terhadap Umkm Di Indonesia,” J. Lentera Bisnis, vol. 9, no. 2, p. 109, 2020, doi: 10.34127/jrlab.v9i2.380.

H. P. Monalika and R. Septiyanti, “Intervening Effect of Information Technology on Msmes Performance during Covid-19 Pandemic,” vol. 3, no. 1, pp. 1–14, 2022.

A. P. J. I. Indonesia, “Laporan Survei Internet APJII 2019 – 2020 (Q2),” Jakarta, 2020. [Online]. Available: https://apjii.or.id/survei/surveiinternetapjii20192020q2-21072046.

L. J. Anreaja, N. N. Harefa, J. G. P. Negara, V. N. H. Pribyantara, and A. B. Prasetyo, “Naive Bayes and Support Vector Machine Algorithm for Sentiment Analysis Opensea Mobile Application Users in Indonesia,” JISA(Jurnal Inform. dan Sains), vol. 5, no. 1, pp. 62–68, 2022, doi: 10.31326/jisa.v5i1.1267.

R. Meifitrah, I. Darmawan, and O. Nurul Pratiwi, “Sentiment analysis of tokopedia application review to service product recommender system using neural collaborative filtering for marketplace in Indonesia,” IOP Conf. Ser. Mater. Sci. Eng., vol. 909, no. 1, 2020, doi: 10.1088/1757-899X/909/1/012071.

B. Gunawan, H. S. Pratiwi, and E. E. Pratama, “Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes,” J. Edukasi dan Penelit. Inform., vol. 4, no. 2, p. 113, 2018, doi: 10.26418/jp.v4i2.27526.

V. Y. Dronov and G. A. Dronova, “Python as an automation tool in IS. Protecting Database Access in Python,” J. Phys. Conf. Ser., vol. 2182, no. 1, p. 012093, Mar. 2022, doi: 10.1088/1742-6596/2182/1/012093.

M. Yang, B. Jiang, Y. Wang, T. Hao, and Y. Liu, “News Text Mining-Based Business Sentiment Analysis and Its Significance in Economy,” Front. Psychol., vol. 13, no. July, pp. 1–7, 2022, doi: 10.3389/fpsyg.2022.918447.

U. Rhohmawati, I. Slamet, and H. Pratiwi, “Sentiment Analysis Using Maximum Entropy on Application Reviews (Study Case: Shopee on Google Play),” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 5, no. 1, pp. 44–49, 2019, doi: 10.26555/jiteki.v5i1.13087.

P. A. Aritonang, M. E. Johan, and I. Prasetiawan, “Aspect-Based Sentiment Analysis on Application Review using Convolutional Neural Network,” Ultim. InfoSysJ. Ilmu Sist. Inf., vol. 13, no. 1, pp. 54–61, 2022, doi: 10.31937/si.v13i1.2684.

A. Miftahusalam, A. F. Nuraini, A. A. Khoirunisa, and H. Pratiwi, “Perbandingan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine Pada Analisis Sentimen Twitter Mengenai Opini Masyarakat Terhadap Penghapusan Tenaga Honorer,” in Seminar Nasional Official Statistics, 2022, vol. 2022, no. 1, pp. 563–572, [Online]. Available: https://prosiding.stis.ac.id/index.php/semnasoffstat/article/view/1410.

A. Nayak and S. Natarajan, “Comparative study of Naïve Bayes , Support Vector Machine and Random Forest Classifiers in Sentiment Analysis of Twitter feeds,” Int. J. Adv. Stud. Comput. Sci. Eng. IJASCSE, vol. 5, no. 1, pp. 14–17, 2016.

Q. Lv, W. Feng, Y. Quan, G. Dauphin, L. Gao, and M. Xing, “Enhanced-Random-Feature-Subspace-Based Ensemble CNN for the Imbalanced Hyperspectral Image Classification,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 14, pp. 3988–3999, 2021, doi: 10.1109/JSTARS.2021.3069013.

Z. Wu, W. Lin, Z. Zhang, A. Wen, and L. Lin, “An Ensemble Random Forest Algorithm for Insurance Big Data Analysis,” Proc. - 2017 IEEE Int. Conf. Comput. Sci. Eng. IEEE/IFIP Int. Conf. Embed. Ubiquitous Comput. CSE EUC 2017, vol. 1, pp. 531–536, 2017, doi: 10.1109/CSE-EUC.2017.99.

Kaggle, shopee review indonesian. 2021.

S. Khomsah, A. F. Hidayatullah, and A. S. Aribowo, “Comparison of the Effects of Feature Selection and Tree-Based Ensemble Machine Learning for Sentiment Analysis on Indonesian YouTube Comments,” 2021, pp. 161–172.

V. A. Fitri, R. Andreswari, and M. A. Hasibuan, “Sentiment analysis of social media Twitter with case of Anti-LGBT campaign in Indonesia using Naïve Bayes, decision tree, and random forest algorithm,” Procedia Comput. Sci., vol. 161, pp. 765–772, 2019, doi: 10.1016/j.procs.2019.11.181.

L. B. Ilmawan and E. Winarko, “Aplikasi Mobile untuk Analisis Sentimen pada Google Play,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 9, no. 1, p. 53, 2015, doi: 10.22146/ijccs.6640.

A. Maulana and H. Pratiwi, “Sentiment analysis of public towards infrastructure development in Indonesia on Twitter media using boosting support vector machine method,” AIP Conf. Proc., vol. 2202, no. 2019, 2019, doi: 10.1063/1.5141695.


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