Money talks, happiness walks: dissecting the secrets of global bliss with machine learning

Jaiswal, Rachana; Gupta, Shashank

doi:10.1080/14765284.2023.2245277

Cited by 4 publications

(1 citation statement)

References 144 publications

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“…However, if we talk about lexicon models, we can find that TextBlob outperformed the VADER model for sentiment analysis. As far as traditional ML models (LR, SVM, XGBoost, RF and NB) are concerned, almost all models result in a similar performance (Jaiswal et al , 2023; Jaiswal and Gupta, 2023; Jaiswal, 2022). However, RF performed better (Vohra and Garg, 2023; Jaiswal et al , 2023) among them, but with a slight margin of ∼5%.…”

Section: Resultsmentioning

confidence: 99%

Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning

Jaiswal,

Gupta,

Tiwari

2024

MRR

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Purpose Grounded 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/approach Using 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. Findings Gibbs 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/implications This 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 implications Leveraging 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 implications By 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/value This 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.

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Section: Resultsmentioning

confidence: 99%

Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning

Jaiswal,

Gupta,

Tiwari

2024

MRR

Self Cite

View full text Add to dashboard Cite

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Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis

Peng,

Bakkar,

et al. 2024

Transportation Research Part A: Policy and Practice

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Digital nomads: a systematic literature review and future research agenda

Gupta,

Jaiswal,

Gupta

2024

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Purpose This study aims to address the need for robust conceptual foundations in the digital nomadism discourse through a systematic literature review (SLR). It delves into digital nomadism within sustainable tourism, providing valuable insights to foster community development. Design/methodology/approach This study uses a rigorous eight-step process that combines an SLR and a system dynamics approach. In the SLR phase, the authors use the theory, context, characteristics and methodology framework to identify key variables for a dynamic model of sustainable tourism through digital nomadism, following detailed search and selection criteria based on PRISMA guidelines. The second phase uses causal loop diagrams (CLDs) from the literature review to visualize relationships and inform future research directions. The CLD model is validated through literature-based and stakeholder interaction processes, focusing on the social, economic and environmental dimensions, resulting in the development of a robust conceptual model. Findings This study identified 28 theories, including agency–structure theory, travel career ladder theory, stakeholder theory and grounded theory, among others. Semi-structured interviews were the primary research method. Major themes explored encompass community interactions, work lifestyles, cultural aspects, financial considerations, infrastructure and policy. The proposed conceptual model aims to scrutinize relationships across economic, environmental and social dimensions, providing a foundation for investigating and promoting sustainable tourism. Research limitations/implications Policy implications involve refining taxation policies to maintain tax bases, promoting collaborative economic models and enforcing environmental regulations. Additionally, integrating community planning infrastructure and implementing demographic strategies to manage potential population shifts are crucial. Policies supporting education, skill transfer, health and well-being initiatives contribute significantly to fostering sustainable tourism practices and enhancing community vitality. Originality/value To the best of authors’ knowledge, this study is the first in the digital nomadism space to propose a conceptual model for sustainable tourism adoption.

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Money talks, happiness walks: dissecting the secrets of global bliss with machine learning

Cited by 4 publications

References 144 publications

Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning

Decoding mood of the Twitterverse on ESG investing: opinion mining and key themes using machine learning

Transportation resilience under Covid-19 Uncertainty: A traffic severity analysis

Digital nomads: a systematic literature review and future research agenda

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