Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse de réseaux textuels× | Analyse des sentiments× | |
|---|---|---|
| Domaine | Fouille de textes | Fouille de textes |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2011 (Paranyushkin); 2005 (Diesner & Carley) | — |
| Auteur d'origine≠ | Dmitry Paranyushkin; Jana Diesner & Kathleen M. Carley | — |
| Type≠ | Text-mining network method | NLP text-classification task |
| Source fondatrice≠ | Paranyushkin, D. (2011). Identifying the Pathways for Meaning Circulation Using Text Network Analysis. Nodus Labs. link ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Alias | semantic network analysis, word co-occurrence network, Metin Ağ Analizi (Text Network Analysis) | opinion mining, polarity detection, duygu analizi |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | Text network analysis models the words or concepts in a text as nodes and their co-occurrences as edges, then uses network metrics to reveal the structure of meaning. The approach was advanced by Diesner and Carley (2005) for communication networks and by Paranyushkin (2011) for tracing the pathways of meaning circulation in text. | 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. |
| ScholarGateJeu de données ↗ |
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