Usporedite metode

	Klasifikacija teksta	Ekstrakcija ključnih riječi	Analiza sentimenta
Područje	Rudarenje teksta	Rudarenje teksta	Rudarenje teksta
Obitelj	Process / pipeline	Process / pipeline	Process / pipeline
Godina nastanka	—	—	—
Tvorac	—	—	—
Vrsta	Supervised NLP classification task	NLP text-mining task	NLP text-classification task
Temeljni izvor	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Drugi nazivi	text categorization, document classification, topic classification, metin sınıflandırma	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	opinion mining, polarity detection, duygu analizi
Srodne	4	4	3
Sažetak	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.	Keyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 2020).	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.