An In-Depth Analysis of Text Clustering Techniques for Identifying Potential Insurance Customers on Social Media: A Machine Learning Perspective
Social media has emerged as a transformative platform for the exchange and dissemination of information. Unlike conventional sources such as online news, social media often offers more real-time and current updates. Effectively harnessing the vast and diverse pool of unstructured data on these p...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
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INTI International University
2023
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/1884/ http://eprints.intimal.edu.my/1884/1/ij2023_71r.pdf http://eprints.intimal.edu.my/1884/2/136 |
| _version_ | 1848766967750590464 |
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| author | Liew, Chun Kin Goh, Ching Pang |
| author_facet | Liew, Chun Kin Goh, Ching Pang |
| author_sort | Liew, Chun Kin |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Social media has emerged as a transformative platform for the exchange and dissemination of
information. Unlike conventional sources such as online news, social media often offers more real-time and current updates. Effectively harnessing the vast and diverse pool of unstructured data on
these platforms requires the extraction of structured information. This research focuses on the
development of a social media web crawler, coupled with the implementation of sophisticated
algorithms like Web Content Mining, Noisy Text Filtering, Named Entity Extraction, Part-Of-Speech (POS) Tagging, and Text Clustering. The aggregated information will be utilized to train
a machine learning model capable of discerning a customer's preferred insurance type—be it
accident, health, car, or life insurance. The overarching objective is to provide insurance
companies with a swift, precise, and cost-effective means of identifying potential customers within
the realm of social media. The result shows that this new technique has successfully identify
relevant topic based on the comments and recommend corresponding insurance to the user |
| first_indexed | 2025-11-14T11:57:52Z |
| format | Article |
| id | intimal-1884 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:59:34Z |
| publishDate | 2023 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-18842025-07-24T09:18:56Z http://eprints.intimal.edu.my/1884/ An In-Depth Analysis of Text Clustering Techniques for Identifying Potential Insurance Customers on Social Media: A Machine Learning Perspective Liew, Chun Kin Goh, Ching Pang Q Science (General) QA76 Computer software Social media has emerged as a transformative platform for the exchange and dissemination of information. Unlike conventional sources such as online news, social media often offers more real-time and current updates. Effectively harnessing the vast and diverse pool of unstructured data on these platforms requires the extraction of structured information. This research focuses on the development of a social media web crawler, coupled with the implementation of sophisticated algorithms like Web Content Mining, Noisy Text Filtering, Named Entity Extraction, Part-Of-Speech (POS) Tagging, and Text Clustering. The aggregated information will be utilized to train a machine learning model capable of discerning a customer's preferred insurance type—be it accident, health, car, or life insurance. The overarching objective is to provide insurance companies with a swift, precise, and cost-effective means of identifying potential customers within the realm of social media. The result shows that this new technique has successfully identify relevant topic based on the comments and recommend corresponding insurance to the user INTI International University 2023-12 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1884/1/ij2023_71r.pdf text en cc_by_4 http://eprints.intimal.edu.my/1884/2/136 Liew, Chun Kin and Goh, Ching Pang (2023) An In-Depth Analysis of Text Clustering Techniques for Identifying Potential Insurance Customers on Social Media: A Machine Learning Perspective. INTI JOURNAL, 2023 (71). pp. 1-6. ISSN e2600-7320 https://intijournal.intimal.edu.my |
| spellingShingle | Q Science (General) QA76 Computer software Liew, Chun Kin Goh, Ching Pang An In-Depth Analysis of Text Clustering Techniques for Identifying Potential Insurance Customers on Social Media: A Machine Learning Perspective |
| title | An In-Depth Analysis of Text Clustering Techniques for Identifying Potential
Insurance Customers on Social Media: A Machine Learning Perspective |
| title_full | An In-Depth Analysis of Text Clustering Techniques for Identifying Potential
Insurance Customers on Social Media: A Machine Learning Perspective |
| title_fullStr | An In-Depth Analysis of Text Clustering Techniques for Identifying Potential
Insurance Customers on Social Media: A Machine Learning Perspective |
| title_full_unstemmed | An In-Depth Analysis of Text Clustering Techniques for Identifying Potential
Insurance Customers on Social Media: A Machine Learning Perspective |
| title_short | An In-Depth Analysis of Text Clustering Techniques for Identifying Potential
Insurance Customers on Social Media: A Machine Learning Perspective |
| title_sort | in-depth analysis of text clustering techniques for identifying potential
insurance customers on social media: a machine learning perspective |
| topic | Q Science (General) QA76 Computer software |
| url | http://eprints.intimal.edu.my/1884/ http://eprints.intimal.edu.my/1884/ http://eprints.intimal.edu.my/1884/1/ij2023_71r.pdf http://eprints.intimal.edu.my/1884/2/136 |