Sentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization

  • Fatihah Rahmadayana Telkom University
  • Yuliant Sibaroni Telkom University
Keywords: sentiment analysis, randomized search, hyperparameter tuning, SVM

Abstract

Government policy on a problematic topic can lead to pros and cons, including the implementation of work from home during the COVID-19 pandemic in Indonesia. Lots of social media users express their opinions through social media, such as Twitter. Using Twitter API, data on Twitter can be obtained freely, so it can be utilized for sentiment analysis. Therefore, this study contains an analysis of public sentiment on the work from home policy using various preprocessing methods and Support Vector Machine with randomized search optimization. The result shows that the use of the acronym expansion method, slang word translation, and emoji translation in the preprocessing stage can increase the F1 Score value. The best F1 score results obtained were 83.362%. The results of the preprocessing method are used to predict unlabeled data. Prediction results show that 62.35% of tweets have positive sentiments, on the contrary, 37.65% of tweets have negative sentiments. So, it can conclude that most netizens support the policy of work from home. 

Downloads

Download data is not yet available.

References

B. Liu, Sentiment analysis and opinion mining. San Rafael: Morgan and Claypool, 2012.

V. I. Santoso, G. Virginia, and Y. Lukito, “Penerapan Sentiment Analysis pada Hasil Evaluasi Dosen dengan Metode Support Vector Machine,” Jurnal Transformatika, vol. 14, no. 2, p. 72, 2017.

H. C. Husada and A. S. Paramita, “Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM),” Teknika, vol. 10, no. 1, pp. 18–26, 2021.

S. K. Lidya, O. S. Sitompul, and S. Efendi, Sentiment Analysis Pada Teks Bahasa Indonesia Menggunakan Support Vector Machine (SVM) Dan K-Nearest Neighbor (K-NN), Mar. 2015.

Z. Jianqiang and G. Xiaolin, “Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis,” IEEE Access, vol. 5, pp. 2870–2879, 2017.

U. Widodo Wijayanto and R. Sarno, “An Experimental Study of Supervised Sentiment Analysis Using Gaussian Naïve Bayes,” 2018 International Seminar on Application for Technology of Information and Communication, 2018.

M. Ahmad, S. Aftab, M. Salman, N. Hameed, I. Ali, and Z. Nawaz, “SVM Optimization for Sentiment Analysis,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 4, 2018.

J. Bergstra and Y. Bengio , “Random Search for Hyper-Parameter Optimization,” Journal of Machine Learning Research 13, pp. 281–305, Feb. 2012.

García, S., Luengo, J., & Herrera, F., “Data Preprocessing In Data Mining.” Cham, Switzerland: Springer International Publishing, 2015.

Ramaprakoso, “Analisis-Sentimen,” GitHub. [Online]. Available: https://github.com/ramaprakoso/analisis-sentimen/blob/master/ kamus/acronym.txt. [Accessed: 20-May-2021].

M. O. Ibrohim and I. Budi, “Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter,” Proceedings of the Third Workshop on Abusive Language Online, 2019.

W. Dai, “Improvement and Implementation of Feature Weighting Algorithm TF-IDF in Text Classification,” Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018), 2018.

Y. Wang and H. Y. Youn, “Feature Weighting Based on Inter-Category and Intra-Category Strength for Twitter Sentiment Analysis,” Applied Sciences, vol. 9, no. 1, p. 92, 2018.

V. N. Vapnik, Statistical learning theory. New York: Wiley, 1998.

Rojas-Dominguez, L. C. Padierna, J. M. Carpio Valadez, H. J. Puga-Soberanes, and H. J. Fraire, “Optimal Hyper-Parameter Tuning of SVM Classifiers With Application to Medical Diagnosis,” IEEE Access, vol. 6, pp. 7164–7176, 2018.

Published
2021-10-25
How to Cite
Fatihah Rahmadayana, & Yuliant Sibaroni. (2021). Sentiment Analysis of Work from Home Activity using SVM with Randomized Search Optimization. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(5), 936 - 942. https://doi.org/10.29207/resti.v5i5.3457
Section
Artikel Teknologi Informasi