ENSEMBLE STACKING DALAM ANALISA SENTIMEN REAKSI VETERAN MILITER AS TERHADAP PENGAMBILALIHAN AFGHANISTAN OLEH TALIBAN

  • Henny Leidiyana (1*) Universitas Bina Sarana Informatika

  • (*) Corresponding Author

Abstract

Abstrak— Sentiment analysis can be used to glean information about user opinions and identify social or political trends. There have been many studies on sentiment analysis using machine learning or lexicon-based methods that have been quite impressive. However, machine learning models often have difficulty generalizing to new data due to various reasons, such as overfitting and limited training data. These models are also prone to bias and variance, which negatively affect the accuracy of their predictions. This study discusses the application of the ensemble stacking method in sentiment analysis with the topic of the takeover of Afghanistan by the Taliban. By monitoring social media, the author uses a dataset in the form of comments on YouTube news channels related to the topic raised. Several studies have shown how the ensemble stacking method predicts better than the single model. The research was carried out by creating a sentiment classification model with logistic regression machine learning algorithms, SVM, KNN, and CART then the ensemble stacking classifier formed by the base learner of the four algorithms. As a result, for a single classifier, the highest average accuracy is the logistic regression algorithm of 74.6 percent. The four algorithms are compiled and predicted by logistic regression, and the stacking ensemble classifier that is applied produces better accuracy than the stand-alone classifier, which is 75.3 percent

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References

Andone, D. (2021). US veterans are disappointed with how the war in Afghanistan is ending and fearful for their Afghan allies.

Ankit, & Saleena, N. (2018). An Ensemble Classification System for Twitter Sentiment Analysis. Procedia Computer Science, 132(Iccids), 937–946. https://doi.org/10.1016/j.procs.2018.05.109

Ansari, M. Z., Aziz, M. B., Siddiqui, M. O., Mehra, H., & Singh, K. P. (2020). Analysis of Political Sentiment Orientations on Twitter. Procedia Computer Science, 167, 1821–1828. https://doi.org/10.1016/j.procs.2020.03.201

BAŞARSLAN, M. S., & KAYAALP, F. (2022). Sentiment Analysis with Ensemble and Machine Learning Methods in Multi-domain and Dataset. Turkish Journal of Engineering, 7(2), 141–148. https://doi.org/10.31127/tuje.1079698

BBC. (2021). Afghanistan Veteran MP Say Taliban Take Over Has Caused Anger, Grief, And Rage. https://youtu.be/chhy1Tdne_Q

Bonta, V., Kumaresh, N., & Janardhan, N. (2019). A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis. Asian Journal of Computer Science and Technology, 8(S2), 1–6. https://doi.org/10.51983/ajcst-2019.8.s2.2037

Gata, W., & Bayhaqy, A. (2020). Analysis sentiment about islamophobia when Christchurch attack on social media. Telkomnika (Telecommunication Computing Electronics and Control), 18(4), 1819–1827. https://doi.org/10.12928/TELKOMNIKA.V18I4.14179

Görmez, Y., Işık, Y. E., Temiz, M., & Aydın, Z. (2020). FBSEM: A Novel Feature-Based Stacked Ensemble Method for Sentiment Analysis. International Journal of Information Technology and Computer Science, 12(6), 11–22. https://doi.org/10.5815/ijitcs.2020.06.02

Lee, E., Rustam, F., Ashraf, I., Washington, P. B., Narra, M., & Shafique, R. (2022). Inquest of Current Situation in Afghanistan Under Taliban Rule Using Sentiment Analysis and Volume Analysis. IEEE Access, 10, 10333–10348. https://doi.org/10.1109/ACCESS.2022.3144659

Mahakul, A. J. M. J. D. J. R. P. P. (2020). Global Perception of the Belt and Road Initiative: A Natural Language Processing Approach. TrT Forum, June, 400. https://doi.org/10.54116/jbdtp.v1i1.18

Matalon, Y., Magdaci, O., Almozlino, A., & Yamin, D. (2021). Using sentiment analysis to predict opinion inversion in Tweets of political communication. Scientific Reports, 11(1), 1–9. https://doi.org/10.1038/s41598-021-86510-w

Muhammad Fikri, A. R., Jondri, J., & Astuti, W. (2022). Sentiment Analysis Against IndiHome and First Media Internet Providers Using Ensemble Stacking Method. Building of Informatics, Technology and Science (BITS), 4(2), 924–931. https://doi.org/10.47065/bits.v4i2.1969

Preeti, Bhardwaj, P., & Kaur, R. (2020). Sentiment analysis using different techniques. International Journal of Advanced Science and Technology, 29(10 Special Issue), 2439–2443.

Priyanka, H. S., & Ashok Kumar, R. (2020). Sentiment Analysis using Machine Learning Based Ensemble Model for Food Reviews. International Journal of Innovative Research in Applied Sciences and Engineering, 4(3), 690–694. https://doi.org/10.29027/ijirase.v4.i3.2020.690-694

Singh, R. (2021). Youtube comments sentiment analysis. May, 0–11.

Sri, M. (2021). Practical Natural Language Processing with Python. In Practical Natural Language Processing with Python. https://doi.org/10.1007/978-1-4842-6246-7

Taliban Duduki Istana Presiden: Perang Afghanistan Berakhir. (n.d.). https://www.cnnindonesia.com/internasional/20210816123846-113-680967/taliban-duduki-istana-presiden-perang-afghanistan-berakhir

Published
2023-08-02
How to Cite
Leidiyana, H. (2023). ENSEMBLE STACKING DALAM ANALISA SENTIMEN REAKSI VETERAN MILITER AS TERHADAP PENGAMBILALIHAN AFGHANISTAN OLEH TALIBAN. INTI Nusa Mandiri, 18(1), 23 - 28. https://doi.org/10.33480/inti.v18i1.4175
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