气候变化领域集成服务门户(CCPortal): Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning

CCPortal

DOI	10.1108/MRR-07-2023-0526
	Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning
	Jaiswal, Rachana; Gupta, Shashank; Tiwari, Aviral Kumar
发表日期	2024
ISSN	2040-8269
EISSN	2040-8277
英文摘要	PurposeGrounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.Design/methodology/approachUsing various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.FindingsGibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as Physical risk of climate change, Employee Health, Safety and well-being and Water management and Scarcity. RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.Research limitations/implicationsThis study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.Practical implicationsLeveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company's ESG standing.Social implicationsBy shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.Originality/valueThis study marks a groundbreaking contribution to scholarly exploration, to the best of the authors' knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.
英文关键词	ESG investing; Sustainable investment; Natural language processing; Sentiment analysis; Topic modeling; Machine learning
语种	英语
WOS研究方向	Business & Economics
WOS类目	Management
WOS记录号	WOS:001188027700001
来源期刊	MANAGEMENT RESEARCH REVIEW
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/303490
作者单位	Hemwati Nandan Bahuguna Garhwal University; Indian Institute of Management (IIM System); Indian Institute of Management Bodh Gaya
推荐引用方式 GB/T 7714	Jaiswal, Rachana,Gupta, Shashank,Tiwari, Aviral Kumar. Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning[J],2024.
APA	Jaiswal, Rachana,Gupta, Shashank,&Tiwari, Aviral Kumar.(2024).Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning.MANAGEMENT RESEARCH REVIEW.
MLA	Jaiswal, Rachana,et al."Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning".MANAGEMENT RESEARCH REVIEW (2024).