Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Diversitat lèxica× | Anàlisi de sentiments× | |
|---|---|---|
| Camp | Mineria de text | Mineria de text |
| Família | Process / pipeline | Process / pipeline |
| Any d'origen | — | — |
| Autor original | — | — |
| Tipus≠ | Text quantification / lexical richness measurement | NLP text-classification task |
| Font seminal≠ | McCarthy, P. M. & Jarvis, S. (2010). MTLD, vocd-D, and HD-D: A validation study of sophisticated approaches to lexical diversity assessment. Behavior Research Methods, 42(2), 381-392. DOI ↗ | Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135. DOI ↗ |
| Àlies | lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi | opinion mining, polarity detection, duygu analizi |
| Relacionats | 3 | 3 |
| Resum≠ | Lexical diversity analysis quantifies how varied the vocabulary of a text is — how rich an author's word choice is — using measures such as the type-token ratio (TTR), MTLD, vocd-D, and Yule's K. The MTLD and vocd-D measures were validated by McCarthy and Jarvis (2010), building on earlier work by Tweedie and Baayen (1998) on the stability of lexical-richness measures. | 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. |
| ScholarGateConjunt de dades ↗ |
|
|