Sentiment Analysis Provider By.U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method

Susanti Fransiska, Rianto Rianto, Acep Irham Gufroni


Provider By.U is a relatively new and attractive telecommunications service with claims to be the first digital provider in Indonesia. All services are done digitally with the By.U application that offers convenience. Even so not all users are satisfied with the service, there are criticisms and suggestions, one of which is delivered through the By.U app review feature on the Google Play Store. Sentiment analysis is performed to extract information related to provider by.U. The steps taken are scrapping review data, positive and negative labeling, preprocessing data including data cleaning, data normalization, stopword removal and negation handling, sentiment classification using Support Vector Machine (SVM) and TF-IDF as feature extraction. TF-IDF+SVM with 5-Fold Validation produces pretty good accuracy with an average accuracy of 84.7%, precision of 84.9%, recall of 84.7%, and f-measure of 84.8%. The highest accuracy results in fold 2, 86.1%. The effect of TF-IDF on the measurement of model performance is not so great, but it is better.


Classification, Provider By.U, Sentiment Analysis, Support Vector Machine, TF-IDF

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Telkomsel. (2019). Telkomsel Luncurkan by.U, Layanan Selular Prabayar Digital End-to-end Pertama di Indonesia. [Online]. Tersedia: [Diakses 7 Februari 2020].

T. A. Lorosae, B. D. Prakoso, Saifudin dan Kusrini. (2018). Analisis Sentimen Berdasarkan Opini Masyarakat Pada Twitter Menggunakan Naive Bayes. Seminar Nasional Teknologi Iinformasi dan Multimedia.

Chauhan C., Sehgal S. (2017). Sentiment Analysis On Product Reviews. International Conference on Computing, Communication and Automation (ICCCA).

S. C. F. Chan and C. W. K. Leung. (2008). Sentiment Analysis of Product Reviews.

R. Moraes, J. F. Valiati and W. P. G. Neto. (2013). Document-Level Sentiment Classification: An Empirical Comparison between SVM and ANN. Elsevier, pp. 621-633, 2013.

S. M. H. Dadgar, M. S. Araghi and M. M. Farahani. (2016). A Novel Text Mining Approach Based on TF-IDF and Support Vector Machine for News Classification. 2nd IEEE International Conference on Engineering and Technology (ICETECH).

S. T. K and J. Shetty. (2017). Sentiment Analysis of Product Reviews: A Review. International Conference on Inventive Communication and Computational Technologies (ICICCT 2017).

G. Patil, V. Galande, V. Kekan and K. Dange. (2014). Sentiment Analysis Using Support Vector Machine. International Journal of Innovative Research in Computer, vol. 2, no. 1.

H. Nguyen, A. Veluchamy, M. Diop and R. Iqbal. (2018). Comparative Study of Sentiment Analysis with Product Reviews Using Machine Learning and Lexicon-Based Approaches. SMU Data Science Review.

A. Kowalczyk. (2017). Support Vector Machines Succinctly. Morrisville, NC: Syncfusion,.Inc.

N. Fikria. (2018). Analisis Klasifikasi Sentimen Review Aplikasi E-Ticketing Menggunakan Metode Support Vector Machine Dan Asosiasi.

U. Makhmudah, S. Bukhori and J. A. Putra. (2019). Sentiment Analysis of Indonesian Homosexual Tweets using Support Vector Machine Method. ICOMITEE.

U. Rofiqoh, R. S. Perdana and M. A. Fauzi. (2017). Analisis Sentimen Tingkat Kepuasan Pengguna Penyedia Layanan Telekomunikasi Seluler Indonesia Pada Twitter Dengan Metode Support Vector Machine dan Lexicon Based Features. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer , vol. 1, pp. 1725-1732.

Y. T. Pratama, F. A. Bachtiar and N. Y. Setiawan. 2018. Analisis Sentimen Opini Pelanggan Terhadap Aspek Pariwisata Pantai Malang Selatan Menggunakan TF-IDF dan Support Vector Machine. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer , vol. 2,.

M. M. J. Soumik, S. S. M. Farhavi, F. Eva, T. Sinha and M. S. Alam,. (2019). Employing Machine Learning techniques on Sentiment Analysis of Google Play Store Bangla reviews. International Conference on Computer and Information Technology (ICCIT).



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