Understanding IMF Decision-Making with Sentiment Analysis

Deniz, Ayça; Angin, Merih; Angin, Pelin

doi:10.1109/siu55565.2022.9864926

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…NLP methods have long been used to automatically process large document collections produced by international organizations. Deniz et al [7] used NLP to automatically classify sentiments in the large document collection of the International Monetary Fund Executive Board meeting minutes, achieving high accuracy when model training was performed with domain-specific data. Sovrano et al [8] proposed an ensemble method for multi-label text classification of UNGA Resolutions, combining nondomain-specific deep learning based document similarities with domain-specific term frequency-inverse document frequency (TF-IDF) document similarities.…”

Section: Related Workmentioning

confidence: 99%

A RoBERTa Approach for Automated Processing of Sustainability Reports

et al. 2022

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There is a strong need and demand from the United Nations, public institutions, and the private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by community volunteers. Results demonstrate that especially fine-tuned RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

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Section: Related Workmentioning

confidence: 99%

A RoBERTa Approach for Automated Processing of Sustainability Reports

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…NLP methods have long been used to automatically process large document collections produced by international organizations. Deniz et al [7] used NLP to automatically classify sentiments in the large document collection of the International Monetary Fund Executive Board meeting minutes achieving high accuracy when model training was performed with domain-specific data. Sovrano et al [8] proposed an ensemble method for multi-label text classification of UNGA Resolutions, combining non domain-specific deep learning based document similarities with domain-specific Term Frequency-Inverse Document Frequency (TF-IDF) document similarities.…”

Section: Related Workmentioning

confidence: 99%

A RoBERTa Approach for Automated Processing of Sustainability Reports

Angin¹,

Taşdemir²,

Yılmaz³

et al. 2022

Preprint

View full text Add to dashboard Cite

There is a strong need and demand from the United Nations, public institutions, and private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by the community volunteers. Results demonstrate that especially RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

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