| Title |
Decarbonization of Turkey: Text Mining Based Topic Modeling for the Literature |
| ID_Doc |
63236 |
| Authors |
Yilmaz, S; Yesil, E; Kaya, T |
| Title |
Decarbonization of Turkey: Text Mining Based Topic Modeling for the Literature |
| Year |
2022 |
| Published |
|
| DOI |
10.1007/978-3-031-09176-6_43 |
| Abstract |
The European Green Deal and Carbon Border Adjustment Mechanism, which both presented at the Paris Climate Conference in 2019, impose new costs on Energy-Intensive and Trade-Exposed sectors in countries that want to export to the European Union and force them into transformation. In this study, to provide a perspective for Turkey, which is one of the largest exporters to the European Union, the use cases realized in the European Union on decarbonization in energy-intensive and trade-exposed sectors are examined with the use of various text mining algorithms and machine learning techniques. A text corpus consisting of 100 different studies on the topic of concern is used for topic modeling with the aid of text mining algorithms such as term frequency and inverse document frequency, hierarchical clustering, and Latent Dirichlet Allocation methodologies for 5-topic generation towards topic-document association procedure. The study provides the fundamental literature-based topic modeling concerning the provided literature of industry-specific decarbonization studies, essential for guiding the future research towards the extracted key points for the policy development of a country under the context. |
| Author Keywords |
Green deal; CBAM; Decarbonization; Latent Dirichlet Allocation; Text mining |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000889132600043 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications |
| Research Area |
Computer Science |
| PDF |
|