Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Leksikaalne mitmekesisus – sõnavara rikkuse mõõtmine× | Sentimentanalüüs× | |
|---|---|---|
| Valdkond | Tekstikaeve | Tekstikaeve |
| Perekond | Process / pipeline | Process / pipeline |
| Tekkeaasta | — | — |
| Looja | — | — |
| Tüüp≠ | Text quantification / lexical richness measurement | NLP text-classification task |
| Algallikas≠ | 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 ↗ |
| Rööpnimetused | lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi | opinion mining, polarity detection, duygu analizi |
| Seotud | 3 | 3 |
| Kokkuvõte≠ | 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. |
| ScholarGateAndmestik ↗ |
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