Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse de fréquence textuelle× | Analyse des sentiments× | |
|---|---|---|
| Domaine | Fouille de textes | Fouille de textes |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1949 | — |
| Auteur d'origine≠ | George K. Zipf (frequency-distribution foundation) | — |
| Type≠ | Descriptive text-mining analysis | NLP text-classification task |
| Source fondatrice≠ | Zipf, G. K. (1949). Human Behavior and the Principle of Least Effort. Addison-Wesley. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Alias | word frequency analysis, n-gram frequency analysis, Metin Frekans Analizi | opinion mining, polarity detection, duygu analizi |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | 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. | Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models. |
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