ScholarGate
Msaidizi
Process / pipeline

Regression ya Maandishi — Kutabiri Nambari Kutoka kwa Maandishi

Regression ya maandishi hutabiri kigezo lengwa kinachoendelea kwa kutumia vipengele vilivyotolewa kutoka kwa maandishi — alama za TF-IDF, embeddings, au n-grams — kama vigezo huru. Kwa kujenga juu ya mpango wa maandishi-kama-data uliowekwa na Gentzkow, Kelly na Taddy (2019), huruhusu matokeo ya nambari kama vile bei, kiwango, au alama ya hisia kuhesabiwa moja kwa moja kutoka kwa hati, na hutumiwa sana katika matumizi ya sayansi ya jamii, uchumi, na fedha.

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Method map

The neighbourhood of related methods — select a node to explore.

Vyanzo

  1. Gentzkow, M., Kelly, B. & Taddy, M. (2019). Text as Data. Journal of Economic Literature, 57(3), 535-574. DOI: 10.1257/jel.20181020
  2. Taddy, M. (2013). Measuring Political Sentiment on Twitter: Factor Optimal Design for Multinomial Inverse Regression. Technometrics, 55(4), 415-425. DOI: 10.1080/00401706.2013.778791

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 1). Text-Based Regression. ScholarGate. https://scholargate.app/sw/text-mining/text-regression

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Imerejelewa na

ScholarGateText Regression (Text-Based Regression). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/text-mining/text-regression · Seti ya data: https://doi.org/10.5281/zenodo.20539026