Linganisha mbinu

	Uondoaji nakala rudufu	Uchanganuzi wa Hisia	Uainishaji wa Maandishi
Nyanja	Uchimbaji wa Matini	Uchimbaji wa Matini	Uchimbaji wa Matini
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	1997	—	—
Mwanzilishi	Andrei Z. Broder (MinHash / Resemblance theory, 1997)	—	—
Aina	Text preprocessing / corpus quality pipeline	NLP text-classification task	Supervised NLP classification task
Chanzo asilia	Broder, A.Z. (1997). On the Resemblance and Containment of Documents. Compression and Complexity of SEQUENCES.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Majina mbadala	near-duplicate detection, document deduplication, corpus deduplication, Metin Tekilleştirme (Near-Duplicate Detection)	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
Zinazohusiana	5	3	4
Muhtasari	Text deduplication is a corpus-quality pipeline that identifies and removes exact and near-duplicate documents from large text collections. Grounded in Andrei Broder's 1997 resemblance theory, it is widely used to improve dataset quality for machine learning model training, search engine indexing, and any downstream NLP task that assumes a non-redundant corpus.	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.