Sammenlign metoder

	Tekstsegmentering	N-gram språkmodell	Sentimentanalyse
Fagfelt	Tekstutvinning	Tekstutvinning	Tekstutvinning
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Opprinnelsesår	1997	—	—
Opphavsperson	Marti A. Hearst (TextTiling)	—	—
Type	NLP document-structure / topic-boundary detection	Statistical language model	NLP text-classification task
Opprinnelig kilde	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.
Alias	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
Relaterte	4	4	3
Sammendrag	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.