Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse morphologique× | Analyse des sentiments× | |
|---|---|---|
| Domaine | Fouille de textes | Fouille de textes |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1980 | — |
| Auteur d'origine≠ | M.F. Porter (Porter stemmer) | — |
| Type≠ | Text-normalisation preprocessing task | NLP text-classification task |
| Source fondatrice≠ | Porter, M.F. (1980). An Algorithm for Suffix Stripping. Program, 14(3), 130-137. DOI ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Alias | stemming, lemmatization, Morfolojik Analiz ve Kök Bulma | opinion mining, polarity detection, duygu analizi |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | Morphological analysis splits words into their stems and affixes so that different surface forms of the same word can be treated as one. It covers two complementary approaches — rule-based stemming, such as the Porter (1980) and Snowball algorithms, and dictionary-aware lemmatization — and is a critical text-normalisation step for agglutinative languages such as Turkish and Arabic. | 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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