Jämför metoder

	Orsanalys	Sentimentanalys	TF-IDF
Ämnesområde	Textutvinning	Textutvinning	Textutvinning
Familj	Process / pipeline	Process / pipeline	Process / pipeline
Ursprungsår	1957	—	1988
Upphovsperson	J.R. Firth (distributional principle)	—	Salton & Buckley
Typ	Text-mining / distributional-semantics technique	NLP text-classification task	Text vectorization / term-weighting scheme
Ursprungskälla	Firth, J.R. (1957). A Synopsis of Linguistic Theory. Studies in Linguistic Analysis. Oxford: Blackwell.	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.
Alias	word co-occurrence, co-occurrence network, Kelime Eş-Oluşum Analizi	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Närliggande	4	3	3
Sammanfattning	Co-occurrence analysis is a text-mining technique that statistically counts the word pairs that appear together within a window or a sentence and uses their frequencies to reveal semantic maps and thematic structure. It rests on the distributional principle articulated by J.R. Firth in 1957 — that a word is characterised by the company it keeps.	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.