{"id":18568,"date":"2025-04-19T06:30:16","date_gmt":"2025-04-19T03:30:16","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesiyle-nefret-soylemi-tespit-uygulamasi-gelistirme-yolculugum\/"},"modified":"2025-04-19T06:30:16","modified_gmt":"2025-04-19T03:30:16","slug":"makine-ogrenmesiyle-nefret-soylemi-tespit-uygulamasi-gelistirme-yolculugum","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesiyle-nefret-soylemi-tespit-uygulamasi-gelistirme-yolculugum\/","title":{"rendered":"Makine \u00d6\u011frenmesiyle Nefret S\u00f6ylemi Tespit Uygulamas\u0131 Geli\u015ftirme Yolculu\u011fum"},"content":{"rendered":"

<\/p>\n

Makine \u00d6\u011frenmesiyle Nefret S\u00f6ylemi Tespit Uygulamas\u0131 Geli\u015ftirme Yolculu\u011fum<\/p>\n

Bu makalede, makine \u00f6\u011frenmesi tekniklerini kullanarak bir nefret s\u00f6ylemi tespit uygulamas\u0131 geli\u015ftirme s\u00fcrecini detayl\u0131 olarak anlataca\u011f\u0131m. \u00d6ncelikle, projenin amac\u0131 ve kapsam\u0131 \u00fczerinde duraca\u011f\u0131m. Ard\u0131ndan, veri toplama, veri \u00f6n i\u015fleme, model se\u00e7imi ve e\u011fitim a\u015famalar\u0131n\u0131 ad\u0131m ad\u0131m a\u00e7\u0131klayaca\u011f\u0131m. Son olarak da, kar\u015f\u0131la\u015ft\u0131\u011f\u0131m zorluklar\u0131 ve elde etti\u011fim sonu\u00e7lar\u0131 payla\u015farak, bu alanda \u00e7al\u0131\u015fanlar i\u00e7in faydal\u0131 bilgiler sunmay\u0131 hedefliyorum. Umar\u0131m bu deneyimlerim, sizlere ilham verecektir.<\/p>\n

Proje Amac\u0131 ve Kapsam\u0131<\/h2>\n

Amac\u0131m, sosyal medya platformlar\u0131nda ve internette yayg\u0131nla\u015fan nefret s\u00f6ylemlerini tespit edebilen bir uygulama geli\u015ftirmekti. Bu uygulama, toplumsal bar\u0131\u015fa katk\u0131da bulunmak ve nefret s\u00f6ylemlerinin yay\u0131lmas\u0131n\u0131 engellemek i\u00e7in kullan\u0131labilecek bir ara\u00e7 olarak tasarland\u0131. Uygulaman\u0131n kapsam\u0131, \u00f6ncelikle T\u00fcrk\u00e7e diline odakl\u0131yd\u0131. Ancak, gelecekte farkl\u0131 dilleri de destekleyecek \u015fekilde geni\u015fletilebilir. Bu kapsamda, veri k\u00fcmesi olu\u015fturmak olduk\u00e7a \u00f6nemli bir a\u015famayd\u0131.<\/p>\n

Veri Toplama ve \u00d6n \u0130\u015fleme<\/h2>\n

Uygulamam\u0131n ba\u015far\u0131s\u0131 i\u00e7in kaliteli ve geni\u015f bir veri k\u00fcmesine ihtiya\u00e7 duyuyordum. Bu nedenle, \u00e7e\u015fitli kaynaklardan nefret s\u00f6ylemi i\u00e7eren ve i\u00e7ermeyen \u00f6rnek c\u00fcmleler toplad\u0131m. Bu s\u00fcre\u00e7 olduk\u00e7a zaman al\u0131c\u0131 ve zahmetliydi. Toplad\u0131\u011f\u0131m verileri temizlemek ve \u00f6n i\u015flemek i\u00e7in ise, noktalama i\u015faretlerini kald\u0131rma, k\u00fc\u00e7\u00fck harfe \u00e7evirme ve gereksiz kelimeleri silme gibi i\u015flemler yapt\u0131m. Ayr\u0131ca, kelime k\u00f6klerine indirgeme (lemmatization) ve kelime g\u00f6mme (word embedding) tekniklerini kullanarak verileri model e\u011fitimine haz\u0131r hale getirdim.<\/p>\n

Model Se\u00e7imi ve E\u011fitimi<\/h2>\n

Nefret s\u00f6ylemi tespiti i\u00e7in \u00e7e\u015fitli makine \u00f6\u011frenmesi modelleri denedim. Bunlar aras\u0131nda, Destek Vekt\u00f6r Makineleri (SVM), Rastgele Ormanlar (Random Forest) ve Derin \u00d6\u011frenme (Deep Learning) modelleri yer al\u0131yordu. Deneyimlerime g\u00f6re, derin \u00f6\u011frenme modelleri \u00f6zellikle b\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde daha iyi performans g\u00f6steriyordu. Bu nedenle, sonu\u00e7 olarak bir Recurrent Neural Network (RNN) modeli olan LSTM (Long Short-Term Memory) a\u011f\u0131 tercih ettim. Modelin e\u011fitimi i\u00e7in, toplad\u0131\u011f\u0131m verileri e\u011fitim ve test k\u00fcmeleri olarak ay\u0131rd\u0131m. Modelin performans\u0131n\u0131 de\u011ferlendirmek i\u00e7in ise, do\u011fruluk (accuracy), hassasiyet (precision) ve geri \u00e7a\u011f\u0131rma (recall) gibi metrikleri kulland\u0131m. Bu s\u00fcre\u00e7te, bir\u00e7ok parametreyle denemeler yaparak en iyi sonu\u00e7lar\u0131 elde etmeye \u00e7al\u0131\u015ft\u0131m. Sonu\u00e7lar olduk\u00e7a ba\u015far\u0131l\u0131yd\u0131.<\/p>\n

\n# \u00d6rnek kod (Python)\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\n# ... di\u011fer k\u00fct\u00fcphaneler ve kodlar ...\n<\/code><\/pre>\n
Kar\u015f\u0131la\u015f\u0131lan Zorluklar ve Sonu\u00e7lar<\/h2>\n
Geli\u015ftirme s\u00fcrecinde,  en b\u00fcy\u00fck zorluklardan biri veri dengesizli\u011fiydi.  Nefret s\u00f6ylemi i\u00e7eren \u00f6rneklerin say\u0131s\u0131, i\u00e7ermeyen \u00f6rneklerin say\u0131s\u0131ndan \u00e7ok daha azd\u0131.  Bu sorunu \u00e7\u00f6zmek i\u00e7in,  veri art\u0131rma (data augmentation) teknikleri ve \u00f6rnek a\u011f\u0131rl\u0131kland\u0131rmas\u0131 kulland\u0131m.  Ayr\u0131ca,  modelin performans\u0131n\u0131 etkileyen ba\u015fka fakt\u00f6rler de vard\u0131.  \u00d6rne\u011fin,  yeni ve nadir kullan\u0131lan kelimeler,  modelin performans\u0131n\u0131 d\u00fc\u015f\u00fcrebiliyordu.  Yine de,  elde etti\u011fim sonu\u00e7lar genel olarak tatmin ediciydi.  Uygulamam,  nefret s\u00f6ylemlerini belirli bir do\u011fruluk oran\u0131yla tespit edebiliyordu.   Bu oran\u0131 daha da art\u0131rmak i\u00e7in \u00e7al\u0131\u015fmalar\u0131m devam etmektedir.<\/p>\n
Sonu\u00e7 ve Gelecek Planlar<\/h2>\n
Bu proje,  makine \u00f6\u011frenmesinin nefret s\u00f6ylemiyle m\u00fccadelede kullan\u0131labilece\u011fini g\u00f6sterdi.  Ancak,  bu alanda daha yap\u0131lacak \u00e7ok i\u015f var.  Gelecekte,  uygulamam\u0131 farkl\u0131 dillere uyarlayarak ve daha geli\u015fmi\u015f modeller kullanarak performans\u0131n\u0131 daha da art\u0131rmay\u0131 hedefliyorum.  Ayr\u0131ca,  uygulamay\u0131 kullan\u0131c\u0131lara daha kullan\u0131c\u0131 dostu hale getirmek i\u00e7in \u00e7al\u0131\u015fmalar yapaca\u011f\u0131m.  Bu \u00e7al\u0131\u015fmalar\u0131m\u0131 kendi web sitemde<\/a> payla\u015fmaya devam edece\u011fim.<\/p>\n
Bu projede,  Do\u011fal Dil \u0130\u015fleme (NLP) kaynaklar\u0131<\/a>  ve Makine \u00d6\u011frenmesi k\u00fct\u00fcphaneleri<\/a>  \u00e7ok yard\u0131mc\u0131 oldu.<\/p>\n
Umar\u0131m bu makale,  nefret s\u00f6ylemi tespiti alan\u0131nda \u00e7al\u0131\u015fanlar ve bu konuya ilgi duyanlar i\u00e7in faydal\u0131 olmu\u015ftur.<\/p>\n
#Etiketler<\/h3>\n
#NefretS\u00f6ylemi #Makine\u00d6\u011frenmesi #UygulamaGeli\u015ftirme #Do\u011falDil\u0130\u015fleme #YapayZeka #FatihSoysal #fatihsoysal.com #LSTM #Derin\u00d6\u011frenme #VeriAnalizi<\/p>\n
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Ard\u0131ndan, veri toplama, veri \u00f6n i\u015fleme, model se\u00e7imi ve e\u011fitim a\u015famalar\u0131n\u0131 ad\u0131m ad\u0131m a\u00e7\u0131klayaca\u011f\u0131m. Son olarak da, kar\u015f\u0131la\u015ft\u0131\u011f\u0131m zorluklar\u0131 ve elde etti\u011fim sonu\u00e7lar\u0131 payla\u015farak, bu alanda \u00e7al\u0131\u015fanlar i\u00e7in faydal\u0131 bilgiler sunmay\u0131 hedefliyorum. Umar\u0131m bu deneyimlerim, sizlere ilham verecektir.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesiyle-nefret-soylemi-tespit-uygulamasi-gelistirme-yolculugum\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Makine \u00d6\u011frenmesiyle Nefret S\u00f6ylemi Tespit Uygulamas\u0131 Geli\u015ftirme Yolculu\u011fum\" \/>\n<meta property=\"og:description\" content=\"Bu makalede, makine \u00f6\u011frenmesi tekniklerini kullanarak bir nefret s\u00f6ylemi tespit uygulamas\u0131 geli\u015ftirme s\u00fcrecini detayl\u0131 olarak anlataca\u011f\u0131m. \u00d6ncelikle, projenin amac\u0131 ve kapsam\u0131 \u00fczerinde duraca\u011f\u0131m. 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