Confronta i metodi

	Rilevamento di parafrasi	Analisi del Sentimento	Classificazione del testo
Campo	Text mining	Text mining	Text mining
Famiglia	Process / pipeline	Process / pipeline	Process / pipeline
Anno di origine	—	—	—
Ideatore	—	—	—
Tipo	NLP sentence-pair classification task	NLP text-classification task	Supervised NLP classification task
Fonte seminale	Dolan, W. B. & Brockett, C. (2005). Automatically Constructing a Corpus of Sentential Paraphrases. Proceedings of the Third International Workshop on Paraphrasing (IWP).	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.
Alias	Parafroz Tespiti (Paraphrase Detection), paraphrase identification, semantic equivalence detection	opinion mining, polarity detection, duygu analizi	text categorization, document classification, topic classification, metin sınıflandırma
Correlati	4	3	4
Sintesi	Paraphrase detection is a natural-language-processing task that decides whether two sentences expressed in different wordings carry the same meaning. The task and its benchmark resources were established by Dolan and Brockett (2005), and it underpins plagiarism detection, question matching, and data deduplication.	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.