Aspect-Based Sentiment Analysis on iPhone Users on Twitter Using the SVM Method and Optimization of Hyperparameter Tuning

Authors

  • I Gusti Ayu Putu Sintha Deviya Yuliani Telkom University, Bandung
  • Yuliant Sibaroni Telkom University, Bandung
  • Erwin Budi Setiawan Telkom University, Bandung

DOI:

https://doi.org/10.30865/mib.v7i1.5430

Keywords:

Aspect Based Sentiment Analysis, iPhone, Support Vector Machine, Hyperparameter Tuning

Abstract

One form of information and communication technology development is a smartphone. Today's popular smartphone products are the iPhone and the social media used to share opinions is Twitter. One of the topics that is often discussed on Twitter is related to iPhone reviews which can refer to different aspects. Therefore, aspect-based sentiment analysis can be applied to iPhone reviews to get more detailed results. This study applies TF-IDF feature extraction as a weighting vocabulary and the Support Vector Machine classification method. This study also uses hyperparameter tuning to optimize parameters to get the best performance. The results of this study obtained the highest accuracy performance results by using the Support Vector Machine classification on the linear kernel and TF-IDF feature extraction on the camera aspect with accuracy 98.07%, battery aspect with accuracy 97.52%, design aspect with accuracy 96.82%, price aspect with accuracy 98.62%, and specification aspect with accuracy 97.07%. As well as getting an increase in the results of the highest accuracy performance by using hyperparameter tuning on the linear kernel for the camera aspect with accuracy 98.07%, battery aspect with accuracy 97.52%, design aspect with accuracy 97.02%, price aspect with accuracy 98.82%, and specification aspect with accuracy 97.22%.

References

A. C. Sari, R. Hartina, R. Awalia, H. Irianti, and N. Ainun, “Komunikasi dan Media Sosial,†J. Messenger, vol. 3, no. 2, p. 69, 2018.

S. A. Evhen and W. Maharani, “Analisis Ulasan Produk pada Media Sosial ( Twitter ) untuk Meningkatkan Kualitas Produk Handphone Menggunakan Metode Aspect-Based dengan Pendekatan Lexicon,†J. Tugas Akhir Fak. Inform., vol. 8, no. 2, pp. 3621–3632, 2021.

S. W. Yudha and M. Wahyudi, “Komparasi Algoritma Klasifikasi Untuk Analisis Sentimen Review Film Berbahasa Asing,†Semin. Nas. Inform. Sist. Inf. Dan Keamanan Siber, pp. 180–185, 2018.

W. Paulina, F. A. Bachtiar, and A. N. Rusydi, “Analisis Sentimen Berbasis Aspek Ulasan Pelanggan Terhadap Kertanegara Premium Guest House Menggunakan Support Vector Machine,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 4, pp. 1141–1149, 2020.

et al., “Klasifikasi Kabupaten Tertinggal di Kawasan Timur Indonesia dengan Support Vector Machine,†JIKO (Jurnal Inform. dan Komputer), vol. 3, no. 3, pp. 188–195, 2020, doi: 10.33387/jiko.v3i3.2364.

F. Resyanto, Y. Sibaroni, and A. Romadhony, “Choosing The Most Optimum Text Preprocessing Method for Sentiment Analysis: Case:iPhone Tweets,†Proc. 2019 4th Int. Conf. Informatics Comput. ICIC 2019, pp. 2–6, 2019, doi: 10.1109/ICIC47613.2019.8985943.

W. Bourequat and H. Mourad, “Sentiment Analysis Approach for Analyzing iPhone Release using Support Vector Machine,†Int. J. Adv. Data Inf. Syst., vol. 2, no. 1, pp. 36–44, 2021, doi: 10.25008/ijadis.v2i1.1216.

J. W. Iskandar and Y. Nataliani, “Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 6, pp. 1120–1126, 2021, doi: 10.29207/resti.v5i6.3588.

R. S. Jagdale, V. S. Shirsat, and S. N. Deshmukh, “Sentiment analysis on product reviews using machine learning techniques,†Adv. Intell. Syst. Comput., vol. 768, pp. 639–647, 2019, doi: 10.1007/978-981-13-0617-4_61.

C. G. Siji George and B. Sumathi, “Grid search tuning of hyperparameters in random forest classifier for customer feedback sentiment prediction,†Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 9, pp. 173–178, 2020, doi: 10.14569/IJACSA.2020.0110920.

M. A. Haqmi Abas, “Agarwood Oil Quality Classification using Support Vector Classifier and Grid Search Cross Validation Hyperparameter Tuning,†Int. J. Emerg. Trends Eng. Res., vol. 8, no. 6, pp. 2551–2556, 2020, doi: 10.30534/ijeter/2020/55862020.

Oryza Habibie Rahman, Gunawan Abdillah, and Agus Komarudin, “Klasifikasi Ujaran Kebencian pada Media Sosial Twitter Menggunakan Support Vector Machine,†J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 17–23, 2021, doi: 10.29207/resti.v5i1.2700.

B. K. Bhavitha, A. P. Rodrigues, and N. N. Chiplunkar, “Comparative study of machine learning techniques in sentimental analysis,†Proc. Int. Conf. Inven. Commun. Comput. Technol. ICICCT 2017, no. Icicct, pp. 216–221, 2017, doi: 10.1109/ICICCT.2017.7975191.

G. Search, “Hyperparameter Tuning pada Algoritma Klasifikasi dengan Grid Search Hyperparameter Tuning on Classification Algorithm with,†vol. 11, pp. 391–401, 2022.

S. Qaiser and R. Ali, “Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents,†Int. J. Comput. Appl., vol. 181, no. 1, pp. 25–29, 2018, doi: 10.5120/ijca2018917395.

E. B. Setiawan, D. H. Widyantoro, and K. Surendro, “Feature expansion for sentiment analysis in twitter,†Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 2018-Octob, pp. 509–513, 2018, doi: 10.1109/EECSI.2018.8752851.

H. Nuraliza, O. N. Pratiwi, and F. Hamami, “Analisis Sentimen IMBd Film Review Dataset Menggunakan Support Vector Machine (SVM) dan Seleksi Feature Importance,†J. Mirai Manaj., vol. 7, no. 1, pp. 1–17, 2022.

W. R. U. Fadilah, D. Agfiannisa, and Y. Azhar, “Analisis Prediksi Harga Saham PT. Telekomunikasi Indonesia Menggunakan Metode Support Vector Machine,†Fountain Informatics J., vol. 5, no. 2, p. 45, 2020, doi: 10.21111/fij.v5i2.4449.

A. Michael, “Komparasi Kombinasi Pre-trained Model dengan SVM pada Klasifikasi Kematangan Kopi Berbasis Citra,†J. Dyn. Saint, vol. 7, no. 1, pp. 42–48, 2022, [Online]. Available: http://journals.ukitoraja.ac.id/index.php/dynamicsaint/article/view/1613

I. Syarif, A. Prugel-Bennett, and G. Wills, “SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance,†TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 14, no. 4, p. 1502, 2016, doi: 10.12928/telkomnika.v14i4.3956.

N. Sakinah, T. Badriyah, and I. Syarif, “Analisis Kinerja Algoritma Mesin Pembelajaran untuk Klarifikasi Penyakit Stroke Menggunakan Citra CT Scan,†J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 4, p. 833, 2020, doi: 10.25126/jtiik.2020743482.

Downloads

Published

2023-01-28

Issue

Vol. 7 No. 1 (2023): Januari 2023

Section

Articles

License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).