পদ্ধতির তুলনা করুন

	টেক্সট সেগমেন্টেশন	অনুভূতি বিশ্লেষণ	TF-IDF
ক্ষেত্র	টেক্সট খনন	টেক্সট খনন	টেক্সট খনন
পরিবার	Process / pipeline	Process / pipeline	Process / pipeline
উদ্ভবের বছর	1997	—	1988
প্রবর্তক	Marti A. Hearst (TextTiling)	—	Salton & Buckley
ধরন	NLP document-structure / topic-boundary detection	NLP text-classification task	Text vectorization / term-weighting scheme
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	4	3	3
সারসংক্ষেপ	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.