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| Analiza częstości występowania tekstu× | Modelowanie tematów× | |
|---|---|---|
| Dziedzina≠ | Eksploracja tekstu | Uczenie głębokie |
| Rodzina≠ | Process / pipeline | Machine learning |
| Rok powstania≠ | 1949 | 1999–2003 |
| Twórca≠ | George K. Zipf (frequency-distribution foundation) | Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003) |
| Typ≠ | Descriptive text-mining analysis | Unsupervised generative probabilistic model |
| Źródło pierwotne≠ | Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley. link ↗ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ |
| Inne nazwy≠ | word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi | Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | Text frequency analysis is a descriptive text-mining method that counts how often words, n-grams, and phrases occur in a corpus to reveal content patterns and dominant themes. It rests on the frequency-distribution insight formalised by George K. Zipf (1949), that a few terms occur very often while most are rare, and it is one of the most basic and widely used entry points into quantitative text analysis. | Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data. |
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