Uncovering employee insights: integrative analysis using structural topic modeling and support vector machines
Online platforms that enable employees to voluntarily share their opinions and experiences about current and former employers present a valuable data source for investigating worker satisfaction. This user-generated feedback has the potential to provide insights that surpass the limitations of tradi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2025
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| Online Access: | http://psasir.upm.edu.my/id/eprint/118381/ http://psasir.upm.edu.my/id/eprint/118381/1/118381.pdf |
| _version_ | 1848867502994489344 |
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| author | Ding, Kai Li, Ruihong Li, Zeyu Hu, Shangui |
| author_facet | Ding, Kai Li, Ruihong Li, Zeyu Hu, Shangui |
| author_sort | Ding, Kai |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Online platforms that enable employees to voluntarily share their opinions and experiences about current and former employers present a valuable data source for investigating worker satisfaction. This user-generated feedback has the potential to provide insights that surpass the limitations of traditional survey methodologies. This study proposes a novel approach by integrating Structural Topic Modeling (STM) analysis with Support Vector Machine (SVM) techniques to scrutinize the robustness of STM findings, particularly concerning the relative significance of extracted topics. This research reveals several insightful observations based on analyzing employee reviews of a large Chinese tech company. Notably, the findings highlight the importance of intangible aspects within the work environment, such as cultural conflicts, leadership style, and perceived fairness, as significant contributors to satisfaction and dissatisfaction among the company employees. Furthermore, this study reveals inconsistencies with prior research on two significant aspects. First, while work-life balance is typically linked to job dissatisfaction, this study suggests that the negative consequences of work-life balance factors can be mitigated by favorable performance in some job satisfaction-related aspects. Second, while monetary rewards undoubtedly exert a considerable influence, they may fail to ensure employee satisfaction when other key aspects of the work experience are underperforming. This research not only contributes to the body of organizational research but also offers practical implications for enhancing employee satisfaction within global tech enterprises. |
| first_indexed | 2025-11-15T14:37:31Z |
| format | Article |
| id | upm-118381 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:37:31Z |
| publishDate | 2025 |
| publisher | Springer Nature |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1183812025-07-09T02:57:25Z http://psasir.upm.edu.my/id/eprint/118381/ Uncovering employee insights: integrative analysis using structural topic modeling and support vector machines Ding, Kai Li, Ruihong Li, Zeyu Hu, Shangui Online platforms that enable employees to voluntarily share their opinions and experiences about current and former employers present a valuable data source for investigating worker satisfaction. This user-generated feedback has the potential to provide insights that surpass the limitations of traditional survey methodologies. This study proposes a novel approach by integrating Structural Topic Modeling (STM) analysis with Support Vector Machine (SVM) techniques to scrutinize the robustness of STM findings, particularly concerning the relative significance of extracted topics. This research reveals several insightful observations based on analyzing employee reviews of a large Chinese tech company. Notably, the findings highlight the importance of intangible aspects within the work environment, such as cultural conflicts, leadership style, and perceived fairness, as significant contributors to satisfaction and dissatisfaction among the company employees. Furthermore, this study reveals inconsistencies with prior research on two significant aspects. First, while work-life balance is typically linked to job dissatisfaction, this study suggests that the negative consequences of work-life balance factors can be mitigated by favorable performance in some job satisfaction-related aspects. Second, while monetary rewards undoubtedly exert a considerable influence, they may fail to ensure employee satisfaction when other key aspects of the work experience are underperforming. This research not only contributes to the body of organizational research but also offers practical implications for enhancing employee satisfaction within global tech enterprises. Springer Nature 2025-02-20 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/118381/1/118381.pdf Ding, Kai and Li, Ruihong and Li, Zeyu and Hu, Shangui (2025) Uncovering employee insights: integrative analysis using structural topic modeling and support vector machines. Journal of Big Data, 12 (1). art. no. 41. pp. 1-30. ISSN 2196-1115; eISSN: 2196-1115 https://journalofbigdata.springeropen.com/articles/10.1186/s40537-025-01100-1 10.1186/s40537-025-01100-1 |
| spellingShingle | Ding, Kai Li, Ruihong Li, Zeyu Hu, Shangui Uncovering employee insights: integrative analysis using structural topic modeling and support vector machines |
| title | Uncovering employee insights: integrative analysis using structural topic modeling and support vector machines |
| title_full | Uncovering employee insights: integrative analysis using structural topic modeling and support vector machines |
| title_fullStr | Uncovering employee insights: integrative analysis using structural topic modeling and support vector machines |
| title_full_unstemmed | Uncovering employee insights: integrative analysis using structural topic modeling and support vector machines |
| title_short | Uncovering employee insights: integrative analysis using structural topic modeling and support vector machines |
| title_sort | uncovering employee insights: integrative analysis using structural topic modeling and support vector machines |
| url | http://psasir.upm.edu.my/id/eprint/118381/ http://psasir.upm.edu.my/id/eprint/118381/ http://psasir.upm.edu.my/id/eprint/118381/ http://psasir.upm.edu.my/id/eprint/118381/1/118381.pdf |