Võrdle meetodeid

	Leksikaalne mitmekesisus – sõnavara rikkuse mõõtmine	Sentimentanalüüs	TF-IDF
Valdkond	Tekstikaeve	Tekstikaeve	Tekstikaeve
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	—	—	1988
Looja	—	—	Salton & Buckley
Tüüp	Text quantification / lexical richness measurement	NLP text-classification task	Text vectorization / term-weighting scheme
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.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Rööpnimetused	lexical richness, vocabulary richness, Sözcüksel Çeşitlilik Analizi	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Seotud	3	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.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.