European Journal of Finance · 2019 · 88 citations · 13 references
BusinessBusiness EconomicsCorporate ReportingCorporate Social ResponsibilityData MiningCorporate Responsibility ReportingCorporate Social Responsibility ReportingSocial ReportingStakeholder ReportingSustainability ReportingTopic ModelingCsr ReportingEmerging Topic DetectionTopic Labeling
This paper performs topic modeling using all publicly available CSR (Corporate Social Responsibility) reports for all constituent firms of the major stock market indices of 15 industrialized countries included in MSCI Europe for the sample period from 1999 to 2016. Our text mining results and LDA analyses indicate that ‘employees safety’, ‘employees training support’, ‘carbon emission’, ‘human right’, ‘efficient power’, and ‘healthcare medicines’ are the common topics reported by publicly listed companies in Europe and the UK. There is a clear sector bias with industrial firms emphasizing ‘employee safety’, Utilities concentrating on ‘efficient power’ while consumer discretionary and consumer staples highlighting ‘food waste’ and ‘food packaging.’ To produce these results, we used a battery of python code to organize the hundreds of reports downloaded from Bloomberg and the internet, the latest R-algorithm to estimate LDA (Latent Dirichlet Allocation) model and the LDAvis interactive tool to visualize and refine the LDA model.
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Applied Physics Letters · 2000 · 3K citations · Full text
Humanities, Biomedical Informatics, Language Documentation +7
LDAvis: A method for visualizing and interpreting topics
Carson Sievert, Kenneth E. Shirley · 2014 · 1.4K citations · Full text