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A hybrid of fuzzy c-means clustering and Latent Dirichlet Allocation for analysing philanthropic corporate social responsibility activities / Nik Siti Madihah Nik Mangsor

To date, philanthropic corporate social responsibility (PCSR) activities are ad-hoc in nature, where assistance is provided more to basic needs with very little attention to activities that can contribute to eradicating poverty. Based on previous related literatures, it is found that there is no pro...

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Main Author: Nik Mangsor, Nik Siti Madihah
Format: Thesis
Language:English
Published: 2023
Online Access:https://ir.uitm.edu.my/id/eprint/88790/1/88790.pdf
https://ir.uitm.edu.my/id/eprint/88790/
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spelling my.uitm.ir.887902023-12-26T07:37:16Z https://ir.uitm.edu.my/id/eprint/88790/ A hybrid of fuzzy c-means clustering and Latent Dirichlet Allocation for analysing philanthropic corporate social responsibility activities / Nik Siti Madihah Nik Mangsor Nik Mangsor, Nik Siti Madihah To date, philanthropic corporate social responsibility (PCSR) activities are ad-hoc in nature, where assistance is provided more to basic needs with very little attention to activities that can contribute to eradicating poverty. Based on previous related literatures, it is found that there is no proper categorization and documentation of CSR-related activities. Conventional clustering algorithms are able to extract only a single set of flat topics in which local and global topics are mingled and cannot be separated. Therefore, this research aims to develop hybrid fuzzy document clustering techniques to improve attribute cluster membership values of PCSR activities. This study has extended document clustering technique by integrating the traditional document clustering application with topic modeling approach. This integrated approach is able to produce precise results where it can help infer more coherent themes of PCSR activities. The analysis involved five-year data from the annual reports of 19 CSR-award winning companies in Malaysia where they were converted into a structured format, collated and summarized. Then, text pre-processing for data cleaning was performed followed by identification of the best Latent Dirichlet Allocation (LDA) topic modelling technique that was used to integrate document clustering. 2023 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/88790/1/88790.pdf A hybrid of fuzzy c-means clustering and Latent Dirichlet Allocation for analysing philanthropic corporate social responsibility activities / Nik Siti Madihah Nik Mangsor. (2023) Masters thesis, thesis, Universiti Teknologi MARA (UiTM).
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
description To date, philanthropic corporate social responsibility (PCSR) activities are ad-hoc in nature, where assistance is provided more to basic needs with very little attention to activities that can contribute to eradicating poverty. Based on previous related literatures, it is found that there is no proper categorization and documentation of CSR-related activities. Conventional clustering algorithms are able to extract only a single set of flat topics in which local and global topics are mingled and cannot be separated. Therefore, this research aims to develop hybrid fuzzy document clustering techniques to improve attribute cluster membership values of PCSR activities. This study has extended document clustering technique by integrating the traditional document clustering application with topic modeling approach. This integrated approach is able to produce precise results where it can help infer more coherent themes of PCSR activities. The analysis involved five-year data from the annual reports of 19 CSR-award winning companies in Malaysia where they were converted into a structured format, collated and summarized. Then, text pre-processing for data cleaning was performed followed by identification of the best Latent Dirichlet Allocation (LDA) topic modelling technique that was used to integrate document clustering.
format Thesis
author Nik Mangsor, Nik Siti Madihah
spellingShingle Nik Mangsor, Nik Siti Madihah
A hybrid of fuzzy c-means clustering and Latent Dirichlet Allocation for analysing philanthropic corporate social responsibility activities / Nik Siti Madihah Nik Mangsor
author_facet Nik Mangsor, Nik Siti Madihah
author_sort Nik Mangsor, Nik Siti Madihah
title A hybrid of fuzzy c-means clustering and Latent Dirichlet Allocation for analysing philanthropic corporate social responsibility activities / Nik Siti Madihah Nik Mangsor
title_short A hybrid of fuzzy c-means clustering and Latent Dirichlet Allocation for analysing philanthropic corporate social responsibility activities / Nik Siti Madihah Nik Mangsor
title_full A hybrid of fuzzy c-means clustering and Latent Dirichlet Allocation for analysing philanthropic corporate social responsibility activities / Nik Siti Madihah Nik Mangsor
title_fullStr A hybrid of fuzzy c-means clustering and Latent Dirichlet Allocation for analysing philanthropic corporate social responsibility activities / Nik Siti Madihah Nik Mangsor
title_full_unstemmed A hybrid of fuzzy c-means clustering and Latent Dirichlet Allocation for analysing philanthropic corporate social responsibility activities / Nik Siti Madihah Nik Mangsor
title_sort hybrid of fuzzy c-means clustering and latent dirichlet allocation for analysing philanthropic corporate social responsibility activities / nik siti madihah nik mangsor
publishDate 2023
url https://ir.uitm.edu.my/id/eprint/88790/1/88790.pdf
https://ir.uitm.edu.my/id/eprint/88790/
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score 13.211869

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