Earticle

다운로드

Sentimental Analysis of Twitter Data Using Machine Learning and Deep Learning : Nickel Ore Export Restrictions to Europe Under Jokowi’s Administration 2022

원문정보

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

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

자료제공 : 네이버학술정보

    간행물 정보

    • 간행물
      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