Earticle

초록

영어

Nowadays, social media has evolved into a powerful networked ecosystem in which governments and citizens publicly debate economic and political issues. This holds true for the pros and cons of Indonesia’s ore nickel export restriction to Europe, which we aim to investigate further in this paper. Using Twitter as a dependable channel for conducting sentiment analysis, we have gathered 7070 tweets data for further processing using two sentiment analysis approaches, namely Support Vector Machine (SVM) and Long Short Term Memory (LSTM). Model construction stage has shown that Bidirectional LSTM performed better than LSTM and SVM kernels, with accuracy of 91%. The LSTM comes second and The SVM Radial Basis Function comes third in terms of best model, with 88% and 83% accuracies, respectively. In terms of sentiments, most Indonesians believe that the nickel ore provision will have a positive impact on the mining industry in Indonesia. However, a small number of Indonesian citizens contradict this policy due to fears of a trade dispute that could potentially harm Indonesia’s bilateral relations with the EU. Hence, this study contributes to the advancement of measuring public opinions through big data tools by identifying Bidirectional LSTM as the optimal model for the dataset.

목차

ABSTRACT
Ⅰ. Introduction
Ⅱ. Conceptual Background
2.1. Twitter and Data Mining in Government Policy
2.2. Sentiment Analysis With Machine Learning and Deep Learning
Ⅲ. Research Methodology
3.1. Dataset
3.2. Model Construction
Ⅳ. Results Analysis
Ⅴ. Discussion and implications
5.1. Discussion of Findings
5.2. Limitations and Future Research Directions
5.3. Implications for Research and Practice
Ⅵ. Conclusion

키워드

저자

• Sophiana Widiastutie [ Assistant Professor, Department of International Relations, Universitas Pembangunan Nasional Veteran Jakarta, Indonesia ]
• Dairatul Maarif [ Assistant Professor, Department of International Relations, Universitas Pembangunan Nasional Veteran Jakarta, Indonesia. Ph.D Student in Asia Pasific Regional Studies, College of Humanities and Social Sciences, National Dong Hwa University Hualien 974, Taiwan ]
• Adinda Aulia Hafizha [ Student in International Relations Studies, Department of International Relations, Universitas Pembangunan Nasional Veteran Jakarta, Indonesia ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658