手法を比較

	テキスト重複排除	感情分析	テキスト分類
分野	テキストマイニング	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	1997	—	—
提唱者	Andrei Z. Broder (MinHash / Resemblance theory, 1997)	—	—
種類	Text preprocessing / corpus quality pipeline	NLP text-classification task	Supervised NLP classification task
原典	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.
別名	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
関連	5	3	4
概要	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.