CriticalMinds: Enhancing ML Models for ESG Impact Analysis Categorisation Using Linguistic Resources and Aspect-Based Sentiment Analysis

Iana Atanassova, Marine Potier, Maya Mathie, Marc Bertin, Panggih Kusuma Ningrum


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.
Anthology ID:
2024.finnlp-1.26
Volume:
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
Editors:
Chung-Chi Chen, Xiaomo Liu, Udo Hahn, Armineh Nourbakhsh, Zhiqiang Ma, Charese Smiley, Veronique Hoste, Sanjiv Ranjan Das, Manling Li, Mohammad Ghassemi, Hen-Hsen Huang, Hiroya Takamura, Hsin-Hsi Chen
Venue:
FinNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
248–253
Language:
URL:
https://aclanthology.org/2024.finnlp-1.26
DOI:
Bibkey:
Cite (ACL):
Iana Atanassova, Marine Potier, Maya Mathie, Marc Bertin, and Panggih Kusuma Ningrum. 2024. CriticalMinds: Enhancing ML Models for ESG Impact Analysis Categorisation Using Linguistic Resources and Aspect-Based Sentiment Analysis. In 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, pages 248–253, Torino, Italia. Association for Computational Linguistics.
Cite (Informal):
CriticalMinds: Enhancing ML Models for ESG Impact Analysis Categorisation Using Linguistic Resources and Aspect-Based Sentiment Analysis (Atanassova et al., FinNLP 2024)
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PDF:
https://aclanthology.org/2024.finnlp-1.26.pdf