Corporate Social Responsibility ReportsCitation formats

Standard

Corporate Social Responsibility Reports : Topic Analysis and Big Data Approach. / Poon, Ser-Huang; Goloshchapova, Irina ; Pritchard, Matthew ; Reed, Philip.

In: European Journal of Finance, 05.12.2018.

Research output: Contribution to journalArticle

Harvard

Poon, S-H, Goloshchapova, I, Pritchard, M & Reed, P 2018, 'Corporate Social Responsibility Reports: Topic Analysis and Big Data Approach' European Journal of Finance.

APA

Poon, S-H., Goloshchapova, I., Pritchard, M., & Reed, P. (Accepted/In press). Corporate Social Responsibility Reports: Topic Analysis and Big Data Approach. European Journal of Finance.

Vancouver

Poon S-H, Goloshchapova I, Pritchard M, Reed P. Corporate Social Responsibility Reports: Topic Analysis and Big Data Approach. European Journal of Finance. 2018 Dec 5.

Author

Poon, Ser-Huang ; Goloshchapova, Irina ; Pritchard, Matthew ; Reed, Philip. / Corporate Social Responsibility Reports : Topic Analysis and Big Data Approach. In: European Journal of Finance. 2018.

Bibtex

@article{111f074602504206a4a9300b5e39df59,
title = "Corporate Social Responsibility Reports: Topic Analysis and Big Data Approach",
abstract = "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.",
keywords = "Corporate Social Responsibility, Environment Social and Governance, Latent Dirichlet Allocation, Topic Modeling",
author = "Ser-Huang Poon and Irina Goloshchapova and Matthew Pritchard and Philip Reed",
year = "2018",
month = "12",
day = "5",
language = "English",
journal = "European Journal of Finance",
issn = "1351-847X",
publisher = "Routledge",

}

RIS

TY - JOUR

T1 - Corporate Social Responsibility Reports

T2 - Topic Analysis and Big Data Approach

AU - Poon, Ser-Huang

AU - Goloshchapova, Irina

AU - Pritchard, Matthew

AU - Reed, Philip

PY - 2018/12/5

Y1 - 2018/12/5

N2 - 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.

AB - 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.

KW - Corporate Social Responsibility

KW - Environment Social and Governance

KW - Latent Dirichlet Allocation

KW - Topic Modeling

M3 - Article

JO - European Journal of Finance

JF - European Journal of Finance

SN - 1351-847X

ER -