{"id":29844,"date":"2021-05-27T13:24:09","date_gmt":"2021-05-27T10:24:09","guid":{"rendered":"https:\/\/www.turhost.com/blog\/?p=29844"},"modified":"2021-11-26T09:51:50","modified_gmt":"2021-11-26T06:51:50","slug":"tf-idf-nedir","status":"publish","type":"post","link":"https:\/\/www.turhost.com/blog\/tf-idf-nedir\/","title":{"rendered":"TF IDF Nedir?"},"content":{"rendered":"\n

TF IDF nedir? sorusunun yan\u0131t\u0131n\u0131, Google’\u0131n ilgili bilgileri saniyeler i\u00e7inde nas\u0131l sunabildi\u011fini ara\u015ft\u0131rarak bulabilir miyiz?<\/p>\n\n\n\n

Do\u011fal Dil \u0130\u015fleme denen NLP (Natural Language Processing), algoritmalar\u0131n metni anlamas\u0131n\u0131 sa\u011flar. \u00c7\u00fcnk\u00fc \u00e7o\u011fu algoritma matematikseldir ve etkili bir \u015fekilde \u00e7al\u0131\u015fmas\u0131 i\u00e7in metnin say\u0131 olarak temsil edilmesine ihtiya\u00e7 duyar. Bunun i\u00e7in de oyuna TF IDF girer.<\/p>\n\n\n\n

TF IDF Nedir?<\/h2>\n\n\n\n

TF-IDF (Term Frequency \u2014 Inverse Document Frequency); terim frekans\u0131 ve ters belge frekans\u0131 anlam\u0131na gelir. Metni anlaml\u0131 say\u0131lar olarak g\u00f6stermenin bir yoludur, vekt\u00f6r g\u00f6sterimi olarak da bilinir. <\/p>\n\n\n\n

TF<\/strong> yani terim frekans\u0131<\/strong> Hans Peter Luhn (1957), IDF<\/strong> yani ters belge frekans\u0131<\/strong> ise Karen Sp\u00e4rck Jones (1972)’un \u00e7al\u0131\u015fmalar\u0131yla ortaya \u00e7\u0131kan metriklerdir. 1970’lerin ba\u015f\u0131nda, bir bilgi eri\u015fim problemini \u00e7\u00f6zmek i\u00e7in kullan\u0131lan TF IDF, o g\u00fcnden bu yana, belge s\u0131n\u0131fland\u0131rmas\u0131, konu modelleme ve durdurma kelimesi filtreleme (stop-word filtering) dahil olmak \u00fczere \u00e7e\u015fitli durumlarda kullan\u0131lan do\u011fal dil i\u015fleme (NLP) algoritmalar\u0131nda rol alm\u0131\u015ft\u0131r.<\/p>\n\n\n\n

Metni vekt\u00f6rle\u015ftirmeye yani say\u0131lara d\u00f6n\u00fc\u015ft\u00fcrmeye yarayan TF IDF ile arama motorlar\u0131n\u0131n algoritmalar\u0131; makaleleri alaka s\u0131ras\u0131na g\u00f6re listeleyebilir. <\/p>\n\n\n\n

TF IDF, s\u00f6zc\u00fc\u011f\u00fcn ortaya \u00e7\u0131kma s\u0131kl\u0131\u011f\u0131 ve incelenen t\u00fcm belgeler (\u00f6rne\u011fin web siteleri) ba\u011flam\u0131ndaki \u00f6nemi hakk\u0131nda bilgi verir. Bu nedenle bu algoritma arama motorlar\u0131 taraf\u0131ndan i\u00e7erik kalitesi de\u011ferlendirme y\u00f6ntemi <\/span>olarak kullan\u0131labilir.<\/p>\n\n\n\n

Bir belgenin en y\u00fcksek puan alan s\u00f6zc\u00fckleri o belgeyle en alakal\u0131 olanlar oldu\u011fundan TF IDF, metindeki anahtar s\u00f6zc\u00fckleri bulmaya yarar. TF IDF; otomatik metin analizi ve makine \u00f6\u011frenimi algoritmalar\u0131nda kelimeleri puanlamak i\u00e7in olduk\u00e7a kullan\u0131\u015fl\u0131d\u0131r. <\/p>\n\n\n\n

TF IDF Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n\n\n\n

TF IDF’nin terim s\u0131kl\u0131\u011f\u0131 ya da terim frekans\u0131<\/span><\/strong> (TF) ve ters belge s\u0131kl\u0131\u011f\u0131 ya da ters belge frekans\u0131<\/span><\/strong> (IDF) olarak adland\u0131r\u0131lan iki bile\u015feni bulunur.<\/p>\n\n\n\n

\"TF
TF-IDF, SERP’lerdeki en de\u011ferli i\u00e7erikleri anlamay\u0131 ve i\u00e7eri\u011fe daha ‘do\u011fal’ bir g\u00f6r\u00fcn\u00fcm vermeyi sa\u011flar.<\/figcaption><\/figure><\/div>\n\n\n\n

Terim s\u0131kl\u0131\u011f\u0131, metinde ya da veri k\u00fcmesinde bulunan her kelimenin ka\u00e7 kez ge\u00e7ti\u011fini yakalar, bir kelimenin belgedeki g\u00f6r\u00fcnme s\u0131kl\u0131\u011f\u0131n\u0131 \u00f6l\u00e7er. \u00d6rne\u011fin, bir makalede \u201cseo\u201d kelimesi 10 defa ge\u00e7iyorsa ve makalenin tamam\u0131 500 kelimeden olu\u015fuyorsa TF de\u011feri 0,02’dir (10\/500).<\/p>\n\n\n\n

IDF, derlemedeki belge say\u0131s\u0131n\u0131n, incelenen anahtar kelimeyi i\u00e7eren topluluktaki belge say\u0131s\u0131na b\u00f6l\u00fcnmesiyle elde edilen logaritmad\u0131r. Yani ters belge s\u0131kl\u0131\u011f\u0131 bir terimin \u00f6nemini \u00f6l\u00e7er, toplam belge say\u0131s\u0131n\u0131n, terimi i\u00e7eren belge say\u0131s\u0131na b\u00f6l\u00fcnmesiyle elde edilir. K\u00fclliyat yani incelenen t\u00fcm belgelerin adedi 10 ise ve test edilen anahtar kelime, k\u00fclliyattaki \u00fc\u00e7 belgede g\u00f6r\u00fcn\u00fcyorsa, bu durumda IDF de\u011feri 0.52’dir (log (10\/3)).<\/p>\n\n\n\n

IDF asl\u0131nda bize kelimenin belge i\u00e7in ne kadar \u00f6nemli oldu\u011funu s\u00f6yler. Bu, o kelimenin t\u00fcm belge setinde ne kadar yayg\u0131n veya nadir oldu\u011fu anlam\u0131na gelir. 0’a ne kadar yak\u0131nsa, kelime o kadar yayg\u0131nd\u0131r. Yani kelime \u00e7ok yayg\u0131nsa ve bir\u00e7ok belgede yer al\u0131yorsa bu say\u0131 0’a yakla\u015facakt\u0131r. Aksi takdirde 1’e yakla\u015facakt\u0131r.
<\/p>\n\n\n\n

Bir kelimenin TF IDF puan\u0131 ne kadar y\u00fcksekse, kelime bulundu\u011fu belgeyle o kadar alakal\u0131d\u0131r.<\/p>\n\n\n\n

TF’yi hesaplarken, t\u00fcm terimler e\u015fit derecede \u00f6nemli kabul edilir. S\u00f6zc\u00fck veri k\u00fcmesinde daha s\u0131k g\u00f6r\u00fcn\u00fcyorsa, belge i\u00e7in o kadar \u00f6nemli olmamakla birlikte terim s\u0131kl\u0131\u011f\u0131 (TF) de\u011feri y\u00fcksektir. ‘Ve’, ‘ile’, ‘e\u011fer’ ‘bu’ gibi gibi her belgede ortak olan belirli terimlerin bir\u00e7ok kez g\u00f6r\u00fcnebilir oldu\u011fu ve veri k\u00fcmesinde daha seyrek kullan\u0131lan s\u00f6zc\u00fcklere k\u0131yasla onlar kadar fazla bilgi ta\u015f\u0131mad\u0131\u011f\u0131 bilinir. <\/p>\n\n\n\n

Bu nedenle, en y\u00fcksek a\u011f\u0131rl\u0131k, bir terimin az say\u0131da belgede bir\u00e7ok kez<\/span> ge\u00e7ti\u011fi zamand\u0131r. En d\u00fc\u015f\u00fck say\u0131, hemen hemen t\u00fcm belgelerde ge\u00e7en terimin bir sonucudur. En y\u00fcksek ile en d\u00fc\u015f\u00fck aras\u0131ndaki a\u011f\u0131rl\u0131klar ise terimin bir belgede daha az g\u00f6r\u00fcnd\u00fc\u011f\u00fcnde veya bir\u00e7ok makalede ge\u00e7ti\u011fini g\u00f6sterir.<\/p>\n\n\n\n

TF IDF negatif de\u011fer alamaz. En d\u00fc\u015f\u00fck de\u011feri 0’d\u0131r. Hem terim s\u0131kl\u0131\u011f\u0131 (TF) hem de ters belge frekans\u0131 (IDF) pozitif say\u0131lard\u0131r. TF IDF, terim s\u0131kl\u0131\u011f\u0131 ile ters belge frekans\u0131 \u00e7arp\u0131m\u0131na e\u015fit oldu\u011fundan, \u00fcr\u00fcn 0’dan k\u00fc\u00e7\u00fck olamaz.<\/p>\n\n\n\n

Bir belgedeki bir kelime i\u00e7in TF IDF hesaplan\u0131rken \u00f6nce her kelimenin s\u0131kl\u0131\u011f\u0131 hesaplan\u0131r. Sonra IDF hesaplan\u0131r. En son TF ve IDF de\u011ferleri \u00e7arp\u0131l\u0131r.<\/p>\n\n\n\n

TF IDF Neden \u00d6nemli?<\/h2>\n\n\n\n

TF-IDF’yi anlamak, TF-IDF’nin \u00fczerine uygulad\u0131\u011f\u0131n\u0131z algoritmalar\u0131n sonu\u00e7lar\u0131n\u0131 anlamay\u0131 ve yorumlamay\u0131 kolayla\u015ft\u0131r\u0131r. Metin s\u0131n\u0131fland\u0131rma problemi NLP’deki yayg\u0131n problemlerden biridir. Metin s\u0131n\u0131fland\u0131rma problemlerinde algoritmalar, e\u011fitim ald\u0131\u011f\u0131 \u00f6nceden tan\u0131mlanm\u0131\u015f bir konu k\u00fcmesine dayal\u0131 olarak konuyu tahmin etmek zorundad\u0131r. <\/p>\n\n\n\n

Birka\u00e7 y\u0131l \u00f6nce Google, kelime say\u0131s\u0131 1500’den az oldu\u011funda, metni temsil etmenin en iyi yolunun TF IDF oldu\u011funu bulmu\u015ftur. Bu yayg\u0131n bir sorun i\u00e7in k\u00fc\u00e7\u00fck bir \u00f6rnekleme sahip oldu\u011funuzda, TF IDF’yi denemenizin i\u015fe yarayaca\u011f\u0131 anlam\u0131na gelir. <\/p>\n\n\n\n

Makine \u00f6\u011frenimi<\/a> ve arama motoru optimizasyonu (SEO) gibi uygulama alanlar\u0131na g\u00f6z atmak, TF IDF’nin \u00f6nemini anlamam\u0131z\u0131 sa\u011flayacakt\u0131r:<\/p>\n\n\n\n

TF IDF ve Makine \u00d6\u011frenimi<\/h3>\n\n\n\n
\"TF
Makine \u00f6\u011frenimi ile metin analizinde, TF-IDF algoritmalar\u0131 verileri kategorilere ay\u0131rmaya ve anahtar kelimeleri \u00e7\u0131karmaya yard\u0131mc\u0131 olur.<\/figcaption><\/figure><\/div>\n\n\n\n

Do\u011fal dille makine \u00f6\u011frenimi, algoritmalar nedeniyle metinleri say\u0131lara d\u00f6n\u00fc\u015ft\u00fcrme zorunlulu\u011funu do\u011furmu\u015ftur. Metin vekt\u00f6rle\u015ftirme; verileri analiz etmek i\u00e7in makine \u00f6\u011frenimi s\u00fcrecinde temel bir ad\u0131md\u0131r.<\/p>\n\n\n\n

Makine \u00f6\u011frenimi algoritmalar\u0131 geleneksel olarak say\u0131larla daha iyi \u00e7al\u0131\u015f\u0131rken, TF-IDF algoritmalar\u0131, onlara say\u0131sal bir de\u011fer veya vekt\u00f6r atayarak kelimeleri de\u015fifre etmelerine yard\u0131mc\u0131 olur. Bu, \u00f6zellikle metin analizi gibi NLP ile ilgili alanlarda makine \u00f6\u011frenimi i\u00e7in devrim niteli\u011finde bir geli\u015fme olmu\u015ftur.<\/p>\n\n\n\n

Kelimeleri makine \u00f6\u011frenimi algoritmalar\u0131n\u0131n anlayabilece\u011fi bir \u015fekilde say\u0131lara d\u00f6n\u00fc\u015ft\u00fcrd\u00fc\u011f\u00fcn\u00fczde, TF-IDF puan\u0131; Naive Bayes ve Destek Vekt\u00f6r Makineleri gibi algoritmalara aktar\u0131labilir. B\u00f6ylece kelime say\u0131s\u0131 gibi daha temel y\u00f6ntemlerin sonu\u00e7lar\u0131 b\u00fcy\u00fck \u00f6l\u00e7\u00fcde iyile\u015ftirilebilir.<\/p>\n\n\n\n

TF-IDF, bir belgedeki her kelimeyi; o belgedeki her kelimenin ne kadar alakal\u0131 oldu\u011funu temsil eden bir numarayla<\/span> ili\u015fkilendirmemize olanak tan\u0131r. <\/p>\n\n\n\n

Bu nedenle TF IDF makine \u00f6\u011frenimi i\u00e7in veri haz\u0131rlama a\u015famas\u0131nda kullan\u0131\u015fl\u0131d\u0131r \u00e7\u00fcnk\u00fc bu a\u015famada, makine \u00f6\u011frenimi algoritmalar\u0131n\u0131n kullanmas\u0131 i\u00e7in kelimelerin say\u0131 olarak kodlanmas\u0131 gerekir.<\/p>\n\n\n\n

Makine \u00f6\u011frenimi algoritmalar\u0131n\u0131n nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 daha iyi anlayabilmeniz i\u00e7in TF-IDF’nin nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 anlamak i\u015fe yarayacakt\u0131r. Makine \u00f6\u011frenimi ile metin analizinde, TF-IDF algoritmalar\u0131 verileri kategorilere ay\u0131rmaya ve anahtar kelimeleri \u00e7\u0131karmaya yard\u0131mc\u0131 olur. Bu, destek biletlerini veya geri bildirim sat\u0131rlar\u0131n\u0131 etiketleme ve veri giri\u015fi gibi basit, monoton g\u00f6revlerin saniyeler i\u00e7inde yap\u0131labilece\u011fi anlam\u0131na gelir. <\/p>\n\n\n\n

TF IDF ve SEO<\/h3>\n\n\n\n
\"TF
SEO amac\u0131yla i\u00e7erik stratejisi geli\u015ftirmek i\u00e7in TF IDF; anahtar kelime ara\u015ft\u0131rmas\u0131ndan rakip analizine kadar bir\u00e7ok noktada de\u011ferli i\u00e7g\u00f6r\u00fclere ula\u015fmaya yarar.<\/figcaption><\/figure><\/div>\n\n\n\n

Arama motorlar\u0131 i\u00e7in bir makalenin, yaz\u0131l\u0131\u015f amac\u0131yla yak\u0131ndan ili\u015fkili t\u00fcm anahtar kelimeleri bulundurmas\u0131 ve do\u011fru uzunlukta olmas\u0131, konuyu eksiksiz bir \u015fekilde kapsad\u0131\u011f\u0131na dair \u00f6nemli bir sinyaldir. Dilerseniz SEO uyumlu makalede anahtar kelime kullan\u0131m\u0131n\u0131n nas\u0131l olmas\u0131 gerekti\u011fini a\u00e7\u0131klad\u0131\u011f\u0131m\u0131z SEO uyumlu makale nedir?<\/a> adl\u0131 yaz\u0131m\u0131za da g\u00f6z atabilirsiniz.<\/p>\n\n\n\n

TF IDF, belirli bir kelime veya kelime \u00f6be\u011finin bir arama motoru i\u00e7in ne kadar de\u011ferli veya \u00f6nemli oldu\u011funu g\u00f6sterir. \u0130\u015fte, i\u00e7eri\u011fe dahil etmek i\u00e7in; Google’\u0131n her bir makalenin konusuyla alakal\u0131 oldu\u011funu d\u00fc\u015f\u00fcnd\u00fc\u011f\u00fc kelimeleri ke\u015ffederken TF IDF algoritmas\u0131n\u0131 kullanabilir ve belirli bir anahtar kelime veya kelime \u00f6be\u011fi i\u00e7in ilk 10 arama sonucunda yer alan sayfalar\u0131 analiz edebilirsiniz. <\/p>\n\n\n\n

Asl\u0131nda TF IDF tek ba\u015f\u0131na bir SEO arac\u0131 de\u011fildir. Ancak TF IDF’yi; terminoloji se\u00e7imlerimizi geni\u015fletmenin ve bir sayfan\u0131n temel kimli\u011fine odaklanman\u0131n bir yolu olarak kullanabiliriz. Hatta bir rakibin i\u00e7erik modellerini inceleme y\u00f6ntemi olarak da TF IDF olduk\u00e7a yararl\u0131d\u0131r .<\/p>\n\n\n\n

Bir TF IDF analizinden bir kelimenin a\u015f\u0131r\u0131 kullan\u0131m\u0131n\u0131n; bir sayfan\u0131n arama motorlar\u0131nda istendi\u011fi gibi s\u0131ralama alamamas\u0131n\u0131n ard\u0131nda yatan neden oldu\u011fu anla\u015f\u0131labilir. <\/p>\n\n\n\n

TF IDF analizi, arama motorlar\u0131nda en \u00fcst s\u0131ralarda yer alan sayfalarda hangi terimlerin kullan\u0131ld\u0131\u011f\u0131n\u0131 g\u00f6stererek, en iyi sitelerin belirli bir konuya nas\u0131l yakla\u015ft\u0131\u011f\u0131n\u0131 anlaman\u0131z\u0131 sa\u011flar. Bu nedenle, TF IDF’yi kullanmak rakiplerin i\u00e7erik ve \u00fcr\u00fcn stratejilerini takip etmenin bir ba\u015fka yoludur.<\/p>\n\n\n\n

\u0130\u00e7erik stratejinizi ileriye ta\u015f\u0131man\u0131za yard\u0131mc\u0131 olmas\u0131 i\u00e7in TF IDF’yi eski bir i\u00e7eri\u011fin amac\u0131n\u0131 de\u011fi\u015ftirmek i\u00e7in de kullanabilirsiniz. Y\u0131llar \u00f6nce yazd\u0131klar\u0131n\u0131z\u0131 g\u00fcncellerken TF IDF’den yararlanmak; i\u00e7eri\u011fin bir k\u0131sm\u0131n\u0131 yeniden kullanmak istedi\u011finizde hangi terimleri, konular\u0131 veya neyin g\u00fcncellenmesi gerekti\u011fini g\u00f6rmenize yard\u0131mc\u0131 olabilir. <\/p>\n\n\n\n

Hatta TF IDF analizinin, \u00f6zellikle \u00fcst s\u0131ralarda g\u00f6r\u00fcnmeyen halihaz\u0131rda yay\u0131nlanm\u0131\u015f makaleler i\u00e7in daha \u00f6nemli oldu\u011funu savunanlar da vard\u0131r. Bu, makalenin daha kaliteli olmas\u0131 ve konuyu daha iyi anlatabilmesi i\u00e7in i\u00e7eri\u011fe eklenmesi gereken eksik kelimeleri ke\u015ffetmenizi sa\u011flar. TF IDF ile optimizasyon, i\u00e7eri\u011finizin d\u00fc\u015f\u00fck kaliteli olarak s\u0131n\u0131fland\u0131r\u0131lma olas\u0131l\u0131\u011f\u0131n\u0131 azalt\u0131r ve konumunu olumlu etkiler.<\/p>\n\n\n\n

Sonu\u00e7 olarak TF IDF’nin, SEO’da pratik ve \u00f6nemli bir kullan\u0131m alan\u0131na sahip oldu\u011funu s\u00f6ylenebilir, \u00f6zellikle y\u00fcksek kaliteli i\u00e7eri\u011fin optimize edilmesinde ve olu\u015fturulmas\u0131nda yararl\u0131d\u0131r. Ancak TF IDF, i\u00e7erik optimizasyonu i\u00e7in tek ba\u015f\u0131na<\/strong> kullan\u0131ld\u0131\u011f\u0131nda ciddi s\u0131n\u0131rlamalarla kar\u015f\u0131 kar\u015f\u0131ya kal\u0131r:<\/p>\n\n\n\n

TF IDF’nin temel s\u0131n\u0131rl\u0131l\u0131\u011f\u0131, bir c\u00fcmleyi anlamada \u00f6nemli olan kelime s\u0131ras\u0131n\u0131 dikkate almamas\u0131d\u0131r. Ayr\u0131ca, belge uzunlu\u011fu, TF IDF de\u011ferlerinde \u00e7ok fazla farkl\u0131l\u0131\u011fa neden olabilir.<\/p>\n\n\n\n

\u00d6zet<\/h2>\n\n\n\n

TF IDF (terim frekans\u0131-ters belge frekans\u0131), sorgu kelimesi ile en alakal\u0131 sonu\u00e7lar\u0131 sunmak i\u00e7in arama motorlar\u0131 taraf\u0131ndan kullan\u0131l\u0131r. Ayr\u0131ca SEO amac\u0131yla i\u00e7erik stratejisi geli\u015ftirmek i\u00e7in TF IDF; anahtar kelime ara\u015ft\u0131rmas\u0131ndan rakip analizine kadar bir\u00e7ok noktada de\u011ferli i\u00e7g\u00f6r\u00fclere ula\u015fmaya yarar. <\/p>\n\n\n\n

\u00d6zetle TF-IDF nedir? sorusunu SERP’lerdeki en de\u011ferli i\u00e7erikleri anlamay\u0131 ve i\u00e7eri\u011fe daha ‘do\u011fal’ bir g\u00f6r\u00fcn\u00fcm vermeyi sa\u011flayan y\u00f6ntem \u015feklinde yan\u0131tlamak m\u00fcmk\u00fcnd\u00fcr.<\/p>\n\n\n\n


<\/p>\n","protected":false},"excerpt":{"rendered":"Metni vekt\u00f6rle\u015ftirmeye yani say\u0131lara d\u00f6n\u00fc\u015ft\u00fcrmeye yarayan TF IDF ile arama motorlar\u0131n\u0131n algoritmalar\u0131; makaleleri alaka s\u0131ras\u0131na g\u00f6re listeleyebilir. Peki TF IDF, i\u00e7eri\u011finiz i\u00e7in ne yapabilir?\n","protected":false},"author":1,"featured_media":40954,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"28488,5226,27054,12706,18030,23857","_relevanssi_noindex_reason":"","footnotes":""},"categories":[138,647,666],"tags":[],"class_list":{"0":"post-29844","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ipuclari","8":"category-kurumsal-hosting","9":"category-sanal-sunucu"},"yoast_head":"\nTF- IDF Nedir? - Geli\u015fmi\u015f \u0130\u00e7 SEO Optimizasyonu - Turhost<\/title>\n<meta name=\"description\" content=\"TF IDF; hem arama motorlar\u0131 i\u00e7in kalite de\u011ferlendirme y\u00f6ntemidir hem de SEO amac\u0131yla rakip analizi ve anahtar kelime se\u00e7iminde rol oynar.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.turhost.com/blog\/tf-idf-nedir\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"TF- IDF Nedir? 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