ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers

Lee, Haein; Lee, Seon Hong; Lee, Kyeo Re; Kim, Jang Hyun

doi:10.32604/cmc.2023.039104

Cited by 16 publications

(8 citation statements)

References 43 publications

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“…ESG metrics provide valuable insights into operational practices and significantly affect investor trust and decision-making when a company embraces sustainable practices and effective governance. The increasing connection between sustainability, ESG metrics, and investor choices underscores the significance of incorporating ESG criteria into financial assessments, thereby merging corporate responsibility with investment strategies 1 , 2 . With this understanding, companies are striving to adopt strategies that enhance social responsibility and environmental impact by integrating ESG information, and these efforts can be reflected to the public through news articles.…”

Section: Discussionmentioning

confidence: 99%

“…Sustainability represents a crucial global trend in shaping the progress of modern enterprises. Given the increasing emphasis on sustainable practices, integrating environmental, social, and governance (ESG) indicators to assess a company’s performance has become indispensable 1 , 2 . ESG indicators measure a company’s ESG performance, providing valuable insights into operational practices 3 .…”

Section: Introductionmentioning

confidence: 99%

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Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

Lee,

Kim,

Jung

2024

Sci Rep

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As sustainability emerges as a crucial factor in the development of modern enterprises, integrating environmental, social, and governance (ESG) information into financial assessments has become essential. ESG indicators serve as important metrics in evaluating a company’s sustainable practices and governance effectiveness, influencing investor trust and future growth potential, ultimately affecting stock prices. This study proposes an innovative approach that combines ESG sentiment index extracted from news with technical indicators to predict the S&P 500 index. By utilizing a deep learning model and exploring optimal window sizes, the study explores the best model through mean absolute percentage error (MAPE) as an evaluation metric. Additionally, an ablation test clarifies the influence of ESG and its causality with the S&P 500 index. The experimental results demonstrate improved predictive accuracy when considering ESG sentiment compared to relying solely on technical indicators or historical data. This comprehensive methodology enhances the advantage of stock price prediction by integrating technical indicators, which consider short-term fluctuations, with ESG information, providing long-term effects. Furthermore, it offers valuable insights for investors and financial market experts, validating the necessity to consider ESG for financial assets and introducing a new perspective to develop investment strategies and decision-making processes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

Lee,

Kim,

Jung

2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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“…Reference [12] employed NLP techniques to translate textual data from social media into ESG scores and utilized BERT for the recognition of ESG risks. In Reference [13], large datasets sourced from newspaper articles and scholarly documents were explored using BERTopic to uncover major keywords and topics related to ESG, proposing a comprehensive ESG management strategy. In Reference [14], the authors investigated past trends in ESG conversations by reviewing corporate earnings call transcripts.…”

Section: Natural Language Processing (Nlp) and Esgmentioning

confidence: 99%

CORRECT? CORECT!: Classification of ESG Ratings with Earnings Call Transcript

2024

KSII TIIS

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While the incorporating ESG indicator is recognized as crucial for sustainability and increased firm value, inconsistent disclosure of ESG data and vague assessment standards have been key challenges. To address these issues, this study proposes an ambiguous text-based automated ESG rating strategy. Earnings Call Transcript data were classified as E, S, or G using the Refinitiv-Sustainable Leadership Monitor's over 450 metrics. The study employed advanced natural language processing techniques such as BERT, RoBERTa, ALBERT, FinBERT, and ELECTRA models to precisely classify ESG documents. In addition, the authors computed the average predicted probabilities for each label, providing a means to identify the relative significance of different ESG factors. The results of experiments demonstrated the capability of the proposed methodology in enhancing ESG assessment criteria established by various rating agencies and highlighted that companies primarily focus on governance factors. In other words, companies were making efforts to strengthen their governance framework. In conclusion, this framework enables sustainable and responsible business by providing insight into the ESG information contained in Earnings Call Transcript data.

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“…By adopting this approach, they achieved a fault location accuracy of up to 92%. Lee et al [19] leveraged extensive data from news articles and academic papers to analyze Environmental, Social and Governance (ESG) discourse. Employing the Bidirectional Encoder Representations from Transformers (BERTopic), the researchers identified significant keywords, and tracked the evolution of topic patters over time.…”

Section: Applications Of Machine Learning In Various Domainsmentioning

confidence: 99%

Robust Sentiment Classification of Metaverse Services Using a Pre-trained Language Model with Soft Voting

2023

KSII TIIS

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Metaverse services generate text data, data of ubiquitous computing, in real-time to analyze user emotions. Analysis of user emotions is an important task in metaverse services. This study aims to classify user sentiments using deep learning and pre-trained language models based on the transformer structure. Previous studies collected data from a single platform, whereas the current study incorporated the review data as "Metaverse" keyword from the YouTube and Google Play Store platforms for general utilization. As a result, the Bidirectional Encoder Representations from Transformers (BERT) and Robustly optimized BERT approach (RoBERTa) models using the soft voting mechanism achieved a highest accuracy of 88.57%. In addition, the area under the curve (AUC) score of the ensemble model comprising RoBERTa, BERT, and A Lite BERT (ALBERT) was 0.9458. The results demonstrate that the ensemble combined with the RoBERTa model exhibits good performance. Therefore, the RoBERTa model can be applied on platforms that provide metaverse services. The findings contribute to the advancement of natural language processing techniques in metaverse services, which are increasingly important in digital platforms and virtual environments. Overall, this study provides empirical evidence that sentiment analysis using deep learning and pre-trained language models is a promising approach to improving user experiences in metaverse services.

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ESG Discourse Analysis Through BERTopic: Comparing News Articles and Academic Papers

Cited by 16 publications

References 43 publications

Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

CORRECT? CORECT!: Classification of ESG Ratings with Earnings Call Transcript

Robust Sentiment Classification of Metaverse Services Using a Pre-trained Language Model with Soft Voting

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