Experimental Evaluation of Dynamic Topic Modeling Algorithms
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866918110320132096 |
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| author | Onah, Ngozichukwuka Steinmetz, Nadine Al-Sayeh, Hani Sattler, Kai-Uwe |
| author_facet | Onah, Ngozichukwuka Steinmetz, Nadine Al-Sayeh, Hani Sattler, Kai-Uwe |
| contents | The amount of text generated daily on social media is gigantic and analyzing this text is useful for many purposes. To understand what lies beneath a huge amount of text, we need dependable and effective computing techniques from self-powered topic models. Nevertheless, there are currently relatively few thorough quantitative comparisons between these models. In this study, we compare these models and propose an assessment metric that documents how the topics change in time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_00710 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Experimental Evaluation of Dynamic Topic Modeling Algorithms Onah, Ngozichukwuka Steinmetz, Nadine Al-Sayeh, Hani Sattler, Kai-Uwe Information Retrieval The amount of text generated daily on social media is gigantic and analyzing this text is useful for many purposes. To understand what lies beneath a huge amount of text, we need dependable and effective computing techniques from self-powered topic models. Nevertheless, there are currently relatively few thorough quantitative comparisons between these models. In this study, we compare these models and propose an assessment metric that documents how the topics change in time. |
| title | Experimental Evaluation of Dynamic Topic Modeling Algorithms |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.00710 |