@inproceedings{atanassova-etal-2024-criticalminds,
title = "{C}ritical{M}inds: Enhancing {ML} Models for {ESG} Impact Analysis Categorisation Using Linguistic Resources and Aspect-Based Sentiment Analysis",
author = "Atanassova, Iana and
Potier, Marine and
Mathie, Maya and
Bertin, Marc and
Ningrum, Panggih Kusuma",
editor = "Chen, Chung-Chi and
Liu, Xiaomo and
Hahn, Udo and
Nourbakhsh, Armineh and
Ma, Zhiqiang and
Smiley, Charese and
Hoste, Veronique and
Das, Sanjiv Ranjan and
Li, Manling and
Ghassemi, Mohammad and
Huang, Hen-Hsen and
Takamura, Hiroya and
Chen, Hsin-Hsi",
booktitle = "Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.finnlp-1.26",
pages = "248--253",
abstract = "This paper presents our method and findings for the ML-ESG-3 shared task for categorising Environmental, Social, and Governance (ESG) impact level and duration. We introduce a comprehensive machine learning framework incorporating linguistic and semantic features to predict ESG impact levels and durations in English and French. Our methodology uses features that are derived from FastText embeddings, TF-IDF vectors, manually crafted linguistic resources, the ESG taxonomy, and aspect-based sentiment analysis (ABSA). We detail our approach, feature engineering process, model selection via grid search, and results. The best performance for this task was achieved by the Random Forest and XGBoost classifiers, with micro-F1 scores of 47.06 {\%} and 65.44 {\%} for English Impact level and Impact length, and 39.04 {\%} and 54.79 {\%} for French Impact level and Impact length respectively.",
}
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<abstract>This paper presents our method and findings for the ML-ESG-3 shared task for categorising Environmental, Social, and Governance (ESG) impact level and duration. We introduce a comprehensive machine learning framework incorporating linguistic and semantic features to predict ESG impact levels and durations in English and French. Our methodology uses features that are derived from FastText embeddings, TF-IDF vectors, manually crafted linguistic resources, the ESG taxonomy, and aspect-based sentiment analysis (ABSA). We detail our approach, feature engineering process, model selection via grid search, and results. The best performance for this task was achieved by the Random Forest and XGBoost classifiers, with micro-F1 scores of 47.06 % and 65.44 % for English Impact level and Impact length, and 39.04 % and 54.79 % for French Impact level and Impact length respectively.</abstract>
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%0 Conference Proceedings
%T CriticalMinds: Enhancing ML Models for ESG Impact Analysis Categorisation Using Linguistic Resources and Aspect-Based Sentiment Analysis
%A Atanassova, Iana
%A Potier, Marine
%A Mathie, Maya
%A Bertin, Marc
%A Ningrum, Panggih Kusuma
%Y Chen, Chung-Chi
%Y Liu, Xiaomo
%Y Hahn, Udo
%Y Nourbakhsh, Armineh
%Y Ma, Zhiqiang
%Y Smiley, Charese
%Y Hoste, Veronique
%Y Das, Sanjiv Ranjan
%Y Li, Manling
%Y Ghassemi, Mohammad
%Y Huang, Hen-Hsen
%Y Takamura, Hiroya
%Y Chen, Hsin-Hsi
%S Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing
%D 2024
%8 May
%I Association for Computational Linguistics
%C Torino, Italia
%F atanassova-etal-2024-criticalminds
%X This paper presents our method and findings for the ML-ESG-3 shared task for categorising Environmental, Social, and Governance (ESG) impact level and duration. We introduce a comprehensive machine learning framework incorporating linguistic and semantic features to predict ESG impact levels and durations in English and French. Our methodology uses features that are derived from FastText embeddings, TF-IDF vectors, manually crafted linguistic resources, the ESG taxonomy, and aspect-based sentiment analysis (ABSA). We detail our approach, feature engineering process, model selection via grid search, and results. The best performance for this task was achieved by the Random Forest and XGBoost classifiers, with micro-F1 scores of 47.06 % and 65.44 % for English Impact level and Impact length, and 39.04 % and 54.79 % for French Impact level and Impact length respectively.
%U https://aclanthology.org/2024.finnlp-1.26
%P 248-253
Markdown (Informal)
[CriticalMinds: Enhancing ML Models for ESG Impact Analysis Categorisation Using Linguistic Resources and Aspect-Based Sentiment Analysis](https://aclanthology.org/2024.finnlp-1.26) (Atanassova et al., FinNLP 2024)
ACL