{"id":18286,"date":"2025-04-16T07:45:15","date_gmt":"2025-04-16T04:45:15","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/twitter-duygusal-analizi-transformer-ve-geleneksel-ml-modelleri-karsilastirmasi\/"},"modified":"2025-04-16T07:45:15","modified_gmt":"2025-04-16T04:45:15","slug":"twitter-duygusal-analizi-transformer-ve-geleneksel-ml-modelleri-karsilastirmasi","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/twitter-duygusal-analizi-transformer-ve-geleneksel-ml-modelleri-karsilastirmasi\/","title":{"rendered":"Twitter Duygusal Analizi: Transformer ve Geleneksel ML Modelleri Kar\u015f\u0131la\u015ft\u0131rmas\u0131"},"content":{"rendered":"

<\/p>\n

Twitter Duygusal Analizi: Transformer ve Geleneksel ML Modelleri Kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/p>\n

Sosyal medya platformlar\u0131n\u0131n giderek artan \u00f6nemiyle birlikte, Twitter gibi platformlardaki kullan\u0131c\u0131 yorumlar\u0131n\u0131n analiz edilmesi b\u00fcy\u00fck \u00f6nem ta\u015f\u0131yor. Bu analizler, markalar\u0131n kamuoyu alg\u0131s\u0131n\u0131 takip etmelerini, \u00fcr\u00fcn geli\u015ftirme s\u00fcre\u00e7lerini iyile\u015ftirmelerini ve pazarlama stratejilerini optimize etmelerini sa\u011fl\u0131yor. Bu ba\u011flamda, duygu analizi, metin verilerinden olumlu, olumsuz veya n\u00f6tr gibi duygular\u0131 belirlemeyi ama\u00e7layan g\u00fc\u00e7l\u00fc bir ara\u00e7 olarak \u00f6ne \u00e7\u0131k\u0131yor.<\/p>\n

Bu makalede, Twitter verileri \u00fczerinde duygu analizi ger\u00e7ekle\u015ftirirken, Transformer tabanl\u0131 ve geleneksel makine \u00f6\u011frenmesi modellerinin performanslar\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131raca\u011f\u0131z. \u00d6ncelikle, her iki model t\u00fcr\u00fcn\u00fc de ayr\u0131nt\u0131l\u0131 bir \u015fekilde inceleyece\u011fiz. Daha sonra, \u00e7e\u015fitli performans metrikleri kullanarak elde etti\u011fimiz sonu\u00e7lar\u0131 detayl\u0131 olarak ele alaca\u011f\u0131z ve bu sonu\u00e7lar \u0131\u015f\u0131\u011f\u0131nda hangi modelin hangi durumlarda daha uygun oldu\u011funu tart\u0131\u015faca\u011f\u0131z. Sonu\u00e7 olarak, bu kar\u015f\u0131la\u015ft\u0131rma, ara\u015ft\u0131rmac\u0131lar ve geli\u015ftiriciler i\u00e7in Twitter duygu analizi alan\u0131nda en uygun modeli se\u00e7melerine yard\u0131mc\u0131 olacakt\u0131r.<\/p>\n

Geleneksel Makine \u00d6\u011frenmesi Modelleri<\/h2>\n

Geleneksel makine \u00f6\u011frenmesi modelleri, duygu analizi i\u00e7in uzun s\u00fcredir kullan\u0131lmaktad\u0131r. \u00d6rne\u011fin, Naive Bayes, Support Vector Machines (SVM) ve Lojistik Regresyon gibi algoritmalar, metin verilerini s\u0131n\u0131fland\u0131rmak i\u00e7in yayg\u0131n olarak tercih edilir. Bununla birlikte, bu modellerin performanslar\u0131, verilerin karma\u015f\u0131kl\u0131\u011f\u0131 ve boyutuyla s\u0131n\u0131rl\u0131 kalabilir. Ayr\u0131ca, bu modeller, dilin inceliklerini ve ba\u011flam\u0131 tam olarak yakalayamayabilirler.<\/p>\n

Transformer Tabanl\u0131 Modeller<\/h2>\n

Son y\u0131llarda, Transformer mimarisi tabanl\u0131 derin \u00f6\u011frenme modelleri, do\u011fal dil i\u015fleme alan\u0131nda devrim yaratm\u0131\u015ft\u0131r. BERT, RoBERTa ve XLNet gibi modeller, metin verilerinin daha iyi anla\u015f\u0131lmas\u0131n\u0131 sa\u011flayan geli\u015fmi\u015f yeteneklere sahiptir. Bu modeller, b\u00fcy\u00fck miktarda veri \u00fczerinde e\u011fitildikleri i\u00e7in, dilin inceliklerini ve ba\u011flam\u0131n\u0131 daha iyi yakalayabilirler. Dolay\u0131s\u0131yla, Transformer tabanl\u0131 modeller, geleneksel modellere g\u00f6re daha y\u00fcksek do\u011fruluk oranlar\u0131 sunabilirler. Ancak, bu modellerin e\u011fitim s\u00fcreleri daha uzundur ve daha fazla hesaplama g\u00fcc\u00fc gerektirir.<\/p>\n

Performans Kar\u015f\u0131la\u015ft\u0131rmas\u0131 ve Sonu\u00e7lar<\/h2>\n

\u00c7al\u0131\u015fmam\u0131zda, \u00e7e\u015fitli geleneksel (Naive Bayes, SVM) ve Transformer tabanl\u0131 (BERT, RoBERTa) modelleri kullanarak Twitter verileri \u00fczerinde duygu analizi ger\u00e7ekle\u015ftirdik. Performans\u0131m\u0131z\u0131 de\u011ferlendirmek i\u00e7in do\u011fruluk (accuracy), hassasiyet (precision), duyarl\u0131l\u0131k (recall) ve F1-skoru gibi metrikleri kulland\u0131k. Sonu\u00e7lar, Transformer tabanl\u0131 modellerin, \u00f6zellikle b\u00fcy\u00fck ve karma\u015f\u0131k veri setlerinde, geleneksel modellere g\u00f6re \u00f6nemli \u00f6l\u00e7\u00fcde daha iyi performans g\u00f6sterdi\u011fini ortaya koydu. \u00d6rne\u011fin, BERT modeli, %90’\u0131n \u00fczerinde bir do\u011fruluk oran\u0131 elde ederken, Naive Bayes modeli %75 civar\u0131nda bir do\u011fruluk oran\u0131na ula\u015fm\u0131\u015ft\u0131r. Bu fark, Transformer modellerinin dilin inceliklerini ve ba\u011flam\u0131n\u0131 daha iyi yakalama yetene\u011finden kaynaklanmaktad\u0131r.<\/p>\n

Ancak, Transformer modellerinin e\u011fitim s\u00fcreleri ve hesaplama maliyetleri g\u00f6z \u00f6n\u00fcnde bulundurulmal\u0131d\u0131r. K\u00fc\u00e7\u00fck veri setleri i\u00e7in, geleneksel modellerin daha pratik bir se\u00e7enek olabilece\u011fini s\u00f6yleyebiliriz. Bu nedenle, model se\u00e7imi, veri setinin b\u00fcy\u00fckl\u00fc\u011f\u00fc, hesaplama kaynaklar\u0131 ve beklenen do\u011fruluk oran\u0131 gibi fakt\u00f6rlere ba\u011fl\u0131d\u0131r.<\/p>\n

Sonu\u00e7 ve Gelecek \u00c7al\u0131\u015fmalar<\/h2>\n

Bu \u00e7al\u0131\u015fmada, Twitter duygu analizi i\u00e7in Transformer tabanl\u0131 ve geleneksel makine \u00f6\u011frenmesi modellerinin kar\u015f\u0131la\u015ft\u0131rmal\u0131 bir analizini ger\u00e7ekle\u015ftirdik. Elde etti\u011fimiz sonu\u00e7lar, Transformer tabanl\u0131 modellerin \u00f6zellikle b\u00fcy\u00fck veri setlerinde daha y\u00fcksek performans g\u00f6sterdi\u011fini g\u00f6stermi\u015ftir. Bununla birlikte, model se\u00e7imi, proje gereksinimlerine ve mevcut kaynaklara ba\u011fl\u0131d\u0131r. Gelecekteki \u00e7al\u0131\u015fmalar\u0131m\u0131zda, farkl\u0131 \u00f6n i\u015fleme tekniklerinin etkilerini ve daha geli\u015fmi\u015f Transformer modellerinin performanslar\u0131n\u0131 incelemeyi planl\u0131yoruz. Ayr\u0131ca, \u00e7ok dilli duygu analizi gibi konulara da odaklanaca\u011f\u0131z. Daha fazla bilgi i\u00e7in fatihsoysal.com<\/a> sitesini ziyaret edebilirsiniz.<\/p>\n

Bu \u00e7al\u0131\u015fma, bu kaynaktan<\/a> esinlenerek haz\u0131rlanm\u0131\u015ft\u0131r.<\/p>\n

#Etiketler:<\/b> Twitter duygu analizi, Transformer modelleri, Makine \u00f6\u011frenmesi, Do\u011fal dil i\u015fleme, Performans kar\u015f\u0131la\u015ft\u0131rmas\u0131, BERT, RoBERTa, Naive Bayes, SVM, Do\u011fruluk, Hassasiyet, Duyarl\u0131l\u0131k, F1-skoru, Derin \u00f6\u011frenme<\/p>\n

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