Sammenlign metoder

	Tekstregression	Tekstklassificering	TF-IDF
Fagområde	Tekstmining	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Oprindelsesår	—	—	1988
Ophavsperson	—	—	Salton & Buckley
Type	Supervised regression on text features	Supervised NLP classification task	Text vectorization / term-weighting scheme
Oprindelig kilde	Gentzkow, M., Kelly, B. & Taddy, M. (2019). Text as Data. Journal of Economic Literature, 57(3), 535-574.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Aliasser	text-as-data regression, predicting numeric outcomes from text, Metin Tabanlı Regresyon	text categorization, document classification, topic classification, metin sınıflandırma	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Relaterede	4	4	3
Resumé	Text-based regression predicts a continuous target variable using features extracted from text — TF-IDF scores, embeddings, or n-grams — as the independent variables. Building on the text-as-data programme consolidated by Gentzkow, Kelly and Taddy (2019), it lets a numeric outcome such as a price, a rating, or a sentiment score be estimated directly from documents, and is widely used in social-science, economics, and finance applications.	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.	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.