השוואת שיטות

	פילוח טקסט	זיהוי שפה (LID)	מודל שפה מסוג N-gram	ניתוח סנטימנט
תחום	כריית טקסט	כריית טקסט	כריית טקסט	כריית טקסט
משפחה	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
שנת המקור	1997	—	—	—
הוגה השיטה	Marti A. Hearst (TextTiling)	—	—	—
סוג	NLP document-structure / topic-boundary detection	NLP text-classification task	Statistical language model	NLP text-classification task
מקור מכונן	Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64.	Lui, M. & Baldwin, T. (2012). langid.py: An Off-the-shelf Language Identification Tool. Proceedings of the ACL 2012 System Demonstrations.	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.
כינויים	topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation)	language detection, LID, Dil Tanımlama (Language Identification)	n-gram model, statistical language model, N-gram Dil Modeli	opinion mining, polarity detection, duygu analizi
קשורות	4	4	4	3
תקציר	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.	Language identification is a natural-language-processing task that automatically detects which language a piece of text is written in. Building on off-the-shelf tools such as langid.py (Lui & Baldwin, 2012) and the efficient classifiers of Joulin et al. (2017), it is widely used to preprocess and filter multilingual data sets.	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.