Green Finance in the Digital Public Sphere: A Multi-Method NLP Analysis
デジタル公共圏におけるグリーンファイナンス：マルチメソッドNLP分析 (AI 翻訳)
M. Kayakuş, Mustafa Terzioğlu, Dilşad Erdoğan, Serdar Paçacı, G. Moiceanu, R. Dobrescu
🤖 gxceed AI 要約
日本語
この研究は、グリーンファイナンスに関するオンラインディスカッションを分析するため、自然言語処理（NLP）のマルチメソッドフレームワークを適用した。X（旧Twitter）から収集した23,452件の投稿をTF-IDF、FinBERT感情分析、LDAトピックモデリング、キーワード共起ネットワーク分析で調査。結果、ポジティブ（44.61%）とニュートラル（45.85%）のセンチメントが支配的であり、気候金融、デジタル金融、エネルギー投資の3つの主要トピックが特定された。
English
This study applies a multi-method NLP framework to analyze green finance discourse on social media. Using TF-IDF, FinBERT sentiment analysis, LDA topic modeling, and keyword co-occurrence networks on 23,452 X posts, it finds that discourse is dominated by positive (44.61%) and neutral (45.85%) sentiment, revealing three main themes: climate finance, digital finance and speculative investment, and energy investments.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGX文脈では、グリーンファイナンスに関するオンライン上の世論分析は、金融機関や企業がサステナビリティコミュニケーション戦略を策定する上で参考になる。ただし、本論文は特定の国に焦点を当てていないため、日本独自の規制や状況との直接的な連関は弱い。それでも、NLP手法の有効性を示す実証例として、日本での同様の分析の応用が期待される。
In the global GX context
For the global GX context, this paper demonstrates the power of NLP methods in understanding public perception of green finance, which is crucial for effective sustainability communication. While not focused on specific regulations like TCFD/ISSB, the insights on sentiment and discourse themes can help financial institutions align their messaging with public expectations and support the broader transition finance agenda.
👥 読者別の含意
🔬研究者:This paper validates a multi-method NLP approach for analyzing green finance discourse, offering a replicable framework for researchers studying sustainability communication.
🏢実務担当者:Companies and financial institutions can use these insights to gauge public sentiment on green finance and refine their sustainability communication strategies.
🏛政策担当者:Policymakers can understand online discourse patterns to identify public concerns or support for green finance policies.
📄 Abstract（原文）
Green finance has become a key component of sustainability transitions by supporting the alignment of financial systems with environmental and climate-related objectives. As discussions on sustainable investments and climate finance increasingly take place in digital environments, analyzing large-scale user-generated content has become important for understanding online discussions and emerging discourse patterns. This study investigates green finance discourse in the digital public sphere using a multi-method Natural Language Processing (NLP) framework. A dataset of 23,452 posts collected from the platform X was analyzed using TF-IDF-based word frequency analysis, FinBERT-based sentiment analysis, Latent Dirichlet Allocation (LDA) topic modelling, and keyword co-occurrence network analysis. The results indicate that green finance discourse is dominated by positive (44.61%) and neutral (45.85%) sentiment, suggesting a generally favorable and institutionalized public perception. Topic modelling identified three dominant themes: climate finance and sustainability transition, digital finance and speculative investment narratives, energy investments and financial infrastructure. Network analysis revealed that sustainability, climate, energy, investment, and finance constitute the core conceptual structure of discourse. These findings demonstrate the effectiveness of NLP-based approaches for analyzing large-scale digital discussions and provide insights for policymakers, financial institutions, and organizations seeking to better understand online discussions surrounding green finance and support more effective sustainability communication strategies.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/app16147315first seen 2026-07-25 05:36:32
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