Bandingkan metode

	Segmentasi Teks	Analisis Sentimen	TF-IDF
Bidang	Penambangan Teks	Penambangan Teks	Penambangan Teks
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	1997	—	1988
Pencetus	Marti A. Hearst (TextTiling)	—	Salton & Buckley
Tipe	NLP document-structure / topic-boundary detection	NLP text-classification task	Text vectorization / term-weighting scheme
Sumber perintis	Hearst, M.A. (1997). TextTiling: Segmenting Text into Multi-Paragraph Subtopic Passages. Computational Linguistics, 23(1), 33-64.	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.
Alias	topic segmentation, discourse segmentation, linear text segmentation, Metin Bölümleme (Text Segmentation)	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Terkait	4	3	3
Ringkasan	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.	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.