Assessing technology legitimacy with topic models and sentiment analysis – The case of wind power in Germany

Dehler-Holland, Joris; Okoh, Marvin; Keles, Dogan

doi:10.1016/j.techfore.2021.121354

Cited by 34 publications

(5 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Researchers no longer need to manually code and interpret data due to statistical validation. As a result, topic modeling has been extensively used in several disciplines, including geography (Shang et al , 2022; Qiao and Williams, 2022), political science (Dehler-Holland et al , 2022; Miranda and Bringula, 2021; Bogović et al , 2021), hospitality and tourism (Shang and Luo, 2022; Wu et al , 2022; Kwon et al , 2022; Mishra et al , 2021; Kirilenko et al , 2021; Lee et al , 2021; Mariani and Baggio, 2021), education (Waheeb et al , 2022; Vijayan, 2021) and medical science (Porturas and Taylor, 2021), among others. Its use in business research, though, has been relatively sparse.…”

Section: Methodsmentioning

confidence: 99%

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

Jaiswal,

Gupta,

Tiwari

2024

MRR

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Jaiswal,

Gupta,

Tiwari

2024

MRR

View full text Add to dashboard Cite

show abstract

“…We analyzed our dataset using a structural topic model (STM) [6,36,37], a natural language processing method for automated content analysis, in order to evaluate the content of our newspaper article collection and to uncover temporal structures in the content. STMs have been successfully used in several studies, including an analysis of the legitimacy of wind-power technology in Germany [38].…”

Section: Structural Topic Models (Stms)mentioning

confidence: 99%

The Impact of the Russian War against Ukraine on the German Hydrogen Discourse

Loewe,

Quittkat,

Knodt

et al. 2024

Sustainability

View full text Add to dashboard Cite

This contribution delves into the transformative effects of the Russian–Ukrainian war on the discourse surrounding German hydrogen. Employing structural topical modeling (STM) on a vast dataset of 2192 newspaper articles spanning from 2019 to 2022, it aims to uncover thematic shifts attributed to the Russian invasion of Ukraine. The onset of the war in February 2022 triggered a significant pivot in the discourse, shifting it from sustainability and climate-change mitigation to the securing of energy supplies through new partnerships, particularly in response to Russia’s unreliability. Germany started exploring alternative energy trading partners like Canada and Australia, emphasizing green hydrogen development. The study illustrates how external shocks can expedite the uptake of new technologies. The adoption of the “H2 readiness” concept for LNG terminals contributes to the successful implementation of green hydrogen. In summary, the Russian–Ukrainian war profoundly impacted the German hydrogen discourse, shifting the focus from sustainability to energy supply security, underscoring the interconnectedness of energy security and sustainability in Germany’s hydrogen policy.

show abstract

“…STM is an innovative probabilistic mixed membership model in the tradition of the latent Dirichlet allocation (Blei et al, 2003). It has become a popular method for example for modeling the framing of newspapers (e.g., Dehler-Holland et al, 2022). The key innovation of STM is "[…] that it permits users to incorporate arbitrary metadata, defined as information about each document, into the topic model" (Roberts et al, 2019, p. 1), such as, in this case, publication date and document source.…”

Section: Structural Topic Modelingmentioning

confidence: 99%

Discursive Struggles Over Pesticide Legitimacy in Switzerland: A News Media Analysis

Kaiser

2023

Preprint

View full text Add to dashboard Cite

Assessing technology legitimacy with topic models and sentiment analysis – The case of wind power in Germany

Cited by 34 publications

References 96 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

The Impact of the Russian War against Ukraine on the German Hydrogen Discourse

Discursive Struggles Over Pesticide Legitimacy in Switzerland: A News Media Analysis

Contact Info

Product

Resources

About