Analisis Sentimen Berbasis Aspek pada Review Female Daily Menggunakan TF-IDF dan Naïve Bayes

 (*)Clarisa Hasya Yutika Mail (Universitas Telkom, Bandung, Indonesia)
 Adiwijaya Adiwijaya (Universitas Telkom, Bandung, Indonesia)
 Said Al Faraby (Universitas Telkom, Bandung, Indonesia)

(*) Corresponding Author



The results of a product review will provide considerable benefits for producers or consumers. Female daily is a forum that discusses beauty products. There are many reviews that are obtained every day. Therefore a technique is needed to analyze the results of the review into valuable information. One of the techniques is aspect-based sentiment analysis. Aspect-based sentiment analysis will analyze each text to identify various aspects (attributes or components) then determine the level of sentiment (positive, negative, or neutral) that is appropriate for each aspect. From the results obtained, there are reviews that use multilingual languages. Then the steps taken are to translate the multilingual language into one language only, namely Indonesian. Before the review is processed, preprocessing will be carried out to make it easier to process. Then the word weighting is done using TF-IDF, and the method for classifying sentiments that will be used is Complement Naïve Bayes to overcome unbalanced data. From the test results obtained the best F1-Score of 62,81% for data translated into English and then into Indonesian and not using stopword removal


Review Female Daily; Aspect-Based Sentiment Analysis; Preprocessing; TF-IDF; Complement Naïve Bayes

Full Text:


Article Metrics

Abstract view : 402 times
PDF - 153 times


Mubarok, M. S., Adiwijaya, & Aldhi, M. D. (2017, August). Aspect-based Sentiment Analysis to Review Products Using Naïve Bayes. AIP Conference Proceedings. Vol. 1867, No. 1, p. 020060. AIP Publishing LLC.

Hu, M., & Liu, B. (2004, August). Mining and Summarizing Customer Reviews. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, (pp. 168-177).

Gojali, S., & Khodra, M. L. (2016, August). Aspect Based Sentiment Analysis for Review Rating Prediction. 2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA) (pp. 1-6). IEEE.

Pugsee, P., Sombatsri, P., & Juntiwakul, R. (2017, May). Satisfactory analysis for cosmetic product review comments. Proceedings of the 2017 International Conference on Data Mining, Communications and Information Technology (pp. 1-6).

Kristiyanti, D. A. (2015). Analisis Sentimen Review Produk Kosmetik menggunakan Algoritma Support Vector Machine dan Particle Swarm Optimization sebagai Metode Seleksi Fitur. SNIT 2015, 1(1), 134-141.

Srividya, K., & Sowjanya, A. M. (2019). Aspect Based Sentiment Analysis using POS Tagging and TFIDF. International Journal of Engineering and Advanced Technology (IJEAT). Volume-8 Issue-6. Blue Eyes Intelligence Engineering & Sciences Publication.

Xhemali, D., Hindie, C. J., & Stone, R. G. (2009, September). Naïve Bayes vs. Decision Trees vs. Neural Networks in the Classification of Training Web Pages. International Journal of Computer Science Issues (IJCSI), Volume 4, Issue 1, pp. 16-23.

Dong, T., Shang, W., & Zhu, H. (2011). An Improved Algorithm of Bayesian Text Categorization. JSW, Volume 6. Issue 9, pp. 1837-1843.

Uysal, A. K., & Gunal, S. (2014). The Impact of Preprocessing on Text Classification. Information Processing and Management, Vol. 50, pp. 104-112.

Ye, J., Jing, X., & Li, J. (2017, September). Sentiment Analysis Using Modified LDA. International conference on signal and information processing, networking and computers (pp. 205-212). Springer, Singapore.

Yulietha, I. M., Faraby, S. A., & Adiwijaya. (2017). Klasifikasi Sentimen Review Film Menggunakan Algoritma Support Vector Machine Sentiment Classification of Movie Reviews Using Algorithm Support Vector Machine. eProceedings of Engineering, Volume 4. Issues 3.

Nugraha, M. (2014). Sentimen Analysis Review Film dengan menggunakan metode KNN. Bandung: Widyatama University.

Raschka, S. (2014). Naive bayes and Text Classification I - Introduction and Theory. arXiv preprint. Retrieved from

Rennie, J. D., Shih, L., Teevan, J., & Karger, D. R. (2003). Tackling the poor assumptions of naive bayes text classifiers. In Proceedings of the 20th international conference on machine learning (ICML-03) (pp. 616-623).

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. The Journal of machine Learning research, 12, 2825-2830.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Analisis Sentimen Berbasis Aspek pada Review Female Daily Menggunakan TF-IDF dan Naïve Bayes


  • There are currently no refbacks.


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

STMIK Budi Darma
Sekretariat : Jln. Sisingamangaraja No. 338 Telp 061-7875998
email :

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