Módszerek összehasonlítása

	Szövegszegmentálás	N-gram nyelvi modell	Szöveges hangulatelemzés	TF-IDF
Tudományterület	Szövegbányászat	Szövegbányászat	Szövegbányászat	Szövegbányászat
Módszercsalád	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Keletkezés éve	1997	—	—	1988
Megalkotó	Marti A. Hearst (TextTiling)	—	—	Salton & Buckley
Típus	NLP document-structure / topic-boundary detection	Statistical language model	NLP text-classification task	Text vectorization / term-weighting scheme
Alapmű	Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64.	Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed.	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.
Alternatív nevek	topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation)	n-gram model, statistical language model, N-gram Dil Modeli	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Kapcsolódó	4	4	3	3
Összefoglaló	Text segmentation divides a long document into meaningful sections (segments) along topic or discourse boundaries. Introduced for subtopic passages by Marti A. Hearst's TextTiling (1997), it supports document-structure analysis and the detection of topic transitions in continuous text.	An n-gram language model is a statistical model that predicts the probability of the next word by looking only at the previous n−1 words. Described in detail by Jurafsky and Martin (Speech and Language Processing), it provides foundational infrastructure for text generation, spelling correction, and speech recognition.	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.