{"id":38142,"date":"2026-01-24T09:00:39","date_gmt":"2026-01-24T06:00:39","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/amazon-comprehend-ile-metin-anlama-aws-yapay-zeka-serisi\/"},"modified":"2026-01-24T09:00:39","modified_gmt":"2026-01-24T06:00:39","slug":"amazon-comprehend-ile-metin-anlama-aws-yapay-zeka-serisi","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/amazon-comprehend-ile-metin-anlama-aws-yapay-zeka-serisi\/","title":{"rendered":"Amazon Comprehend ile Metin Anlama: AWS Yapay Zeka Serisi"},"content":{"rendered":"

Amazon Comprehend ile Metin Anlama: AWS Yapay Zeka Serisi<\/h2>\n

G\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda, her ge\u00e7en g\u00fcn artan devasa metin verileri y\u0131\u011f\u0131n\u0131, i\u015fletmeler i\u00e7in hem bir f\u0131rsat hem de bir zorluk te\u015fkil etmektedir. M\u00fc\u015fteri geri bildirimlerinden sosyal medya yorumlar\u0131na, e-postalardan belgelere kadar her t\u00fcrl\u00fc yaz\u0131l\u0131 i\u00e7eri\u011fi anlamak ve bunlardan de\u011ferli i\u00e7g\u00f6r\u00fcler \u00e7\u0131karmak, rekabet avantaj\u0131 sa\u011flaman\u0131n anahtar\u0131d\u0131r. \u0130\u015fte tam bu noktada, Amazon Web Services (AWS) taraf\u0131ndan sunulan g\u00fc\u00e7l\u00fc bir yapay zeka (AI) hizmeti olan Amazon Comprehend devreye girer. Bu makalede, Amazon Comprehend’in ne oldu\u011funu, nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131, temel \u00f6zelliklerini ve i\u015fletmelerin metin verilerini daha iyi anlamalar\u0131na nas\u0131l yard\u0131mc\u0131 oldu\u011funu detayl\u0131 bir \u015fekilde inceleyece\u011fiz.<\/p>\n

Amazon Comprehend Nedir?<\/h3>\n

Amazon Comprehend, geli\u015ftiricilerin uygulamalar\u0131na kolayca do\u011fal dil i\u015fleme (NLP) yetenekleri eklemesini sa\u011flayan, tam olarak y\u00f6netilen bir makine \u00f6\u011frenimi hizmetidir. B\u00fcy\u00fck miktardaki metin verilerini analiz ederek, anahtar ifadeler, varl\u0131klar, duygu ve dil gibi \u00f6nemli bilgileri otomatik olarak \u00e7\u0131kar\u0131r. Bu sayede, manuel olarak yap\u0131lmas\u0131 gereken zaman al\u0131c\u0131 ve hataya a\u00e7\u0131k metin analizi g\u00f6revlerini otomatize eder ve i\u015fletmelerin daha h\u0131zl\u0131 ve verimli kararlar almas\u0131na olanak tan\u0131r.<\/p>\n

Do\u011fal Dil \u0130\u015fleme (NLP) Nedir?<\/h4>\n

Do\u011fal Dil \u0130\u015fleme (NLP), bilgisayarlar\u0131n insan dilini anlamas\u0131na, yorumlamas\u0131na ve \u00fcretmesine olanak tan\u0131yan bir yapay zeka alan\u0131d\u0131r. Metin analizi, makine \u00e7evirisi, konu\u015fma tan\u0131ma ve duygu analizi gibi bir\u00e7ok uygulama NLP’nin temelini olu\u015fturur. Comprehend, karma\u015f\u0131k NLP algoritmalar\u0131n\u0131 ve makine \u00f6\u011frenimi modellerini arka planda \u00e7al\u0131\u015ft\u0131rarak, kullan\u0131c\u0131lar\u0131n derin NLP uzmanl\u0131\u011f\u0131na sahip olmadan bu yeteneklerden faydalanmas\u0131n\u0131 sa\u011flar.<\/p>\n

Comprehend’in NLP’deki Yeri<\/h4>\n

Amazon Comprehend, karma\u015f\u0131k NLP modellerini kullan\u0131ma haz\u0131r API’ler arac\u0131l\u0131\u011f\u0131yla sunarak, NLP’yi demokratikle\u015ftirir. Geli\u015ftiricilerin s\u0131f\u0131rdan model olu\u015fturmas\u0131na veya b\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde e\u011fitim yapmas\u0131na gerek kalmadan, mevcut metinleri analiz etmelerine olanak tan\u0131r. Bu, \u00f6zellikle k\u00fc\u00e7\u00fck ve orta \u00f6l\u00e7ekli i\u015fletmeler i\u00e7in b\u00fcy\u00fck bir avantajd\u0131r, \u00e7\u00fcnk\u00fc pahal\u0131 altyap\u0131 ve uzman personel ihtiyac\u0131n\u0131 ortadan kald\u0131r\u0131r.<\/p>\n

AWS Yapay Zeka Hizmetleri Ekosistemi<\/h4>\n

Amazon Comprehend, AWS’in geni\u015f yapay zeka ve makine \u00f6\u011frenimi hizmetleri ailesinin bir par\u00e7as\u0131d\u0131r. Bu ekosistemde Amazon Rekognition (g\u00f6r\u00fcnt\u00fc ve video analizi), Amazon Polly (metinden konu\u015fmaya), Amazon Transcribe (konu\u015fmadan metne) ve Amazon SageMaker (\u00f6zel makine \u00f6\u011frenimi modelleri olu\u015fturma) gibi hizmetler bulunur. Comprehend, \u00f6zellikle metin tabanl\u0131 verilerle \u00e7al\u0131\u015fan uygulamalar i\u00e7in vazge\u00e7ilmez bir ara\u00e7t\u0131r ve di\u011fer AWS hizmetleriyle sorunsuz bir \u015fekilde entegre olabilir.<\/p>\n

Amazon Comprehend’in Temel \u00d6zellikleri<\/h3>\n

Amazon Comprehend, metin analizi i\u00e7in \u00e7e\u015fitli g\u00fc\u00e7l\u00fc \u00f6zellikler sunar. Bu \u00f6zellikler, farkl\u0131 i\u015f ihtiya\u00e7lar\u0131na y\u00f6nelik kapsaml\u0131 i\u00e7g\u00f6r\u00fcler sa\u011flar.<\/p>\n

Varl\u0131k Tan\u0131ma (Entity Recognition)<\/h4>\n

Bu \u00f6zellik, metindeki ki\u015fi adlar\u0131, yerler, kurulu\u015flar, tarihler, kredi kart\u0131 numaralar\u0131, \u00fcr\u00fcnler ve olaylar gibi \u00f6nemli varl\u0131klar\u0131 otomatik olarak tan\u0131mlar ve kategorize eder. \u00d6rne\u011fin, bir m\u00fc\u015fteri geri bildiriminde “Amazon” bir kurulu\u015f, “\u0130stanbul” bir yer ve “iPhone 15” bir \u00fcr\u00fcn olarak tan\u0131nabilir.<\/p>\n

Duygu Analizi (Sentiment Analysis)<\/h4>\n

Duygu analizi, bir metnin genel duygu tonunu (olumlu, olumsuz, n\u00f6tr veya kar\u0131\u015f\u0131k) belirler. M\u00fc\u015fteri yorumlar\u0131n\u0131, sosyal medya g\u00f6nderilerini veya \u00fcr\u00fcn incelemelerini analiz ederek, markan\u0131z hakk\u0131ndaki genel alg\u0131y\u0131 veya belirli bir \u00fcr\u00fcnle ilgili memnuniyet d\u00fczeyini anlaman\u0131za yard\u0131mc\u0131 olur.<\/p>\n

Anahtar \u0130fade \u00c7\u0131karma (Key Phrase Extraction)<\/h4>\n

Bu \u00f6zellik, bir metindeki en \u00f6nemli veya en alakal\u0131 anahtar kelimeleri ve ifadeleri \u00e7\u0131kar\u0131r. Uzun metinlerin h\u0131zl\u0131 bir \u00f6zetini olu\u015fturmak veya i\u00e7eri\u011fi indekslemek i\u00e7in kullan\u0131\u015fl\u0131d\u0131r. \u00d6rne\u011fin, bir haber makalesinden “yapay zeka geli\u015fmeleri” veya “k\u00fcresel ekonomi” gibi anahtar ifadeler \u00e7\u0131kar\u0131labilir.<\/p>\n

Dil Tespiti (Language Detection)<\/h4>\n

Comprehend, giri\u015f metninin yaz\u0131ld\u0131\u011f\u0131 dili otomatik olarak alg\u0131lar. Bu, \u00f6zellikle \u00e7ok dilli i\u00e7eriklerle \u00e7al\u0131\u015fan uygulamalar i\u00e7in kritik bir \u00f6zelliktir. E-postalar\u0131, m\u00fc\u015fteri destek taleplerini veya web sitelerini do\u011fru dile y\u00f6nlendirmek i\u00e7in kullan\u0131labilir.<\/p>\n

S\u00f6z Dizimi Analizi (Syntax Analysis)<\/h4>\n

S\u00f6z dizimi analizi, metindeki her kelimenin dilbilgisel yap\u0131s\u0131n\u0131 (\u00f6rne\u011fin, isim, fiil, s\u0131fat) ve di\u011fer kelimelerle olan ili\u015fkisini belirler. Bu, metnin daha derinlemesine anla\u015f\u0131lmas\u0131 ve daha karma\u015f\u0131k NLP g\u00f6revleri i\u00e7in temel olu\u015fturur.<\/p>\n

Amazon Comprehend Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h3>\n

Amazon Comprehend, AWS’in y\u00fcksek \u00f6l\u00e7eklenebilir altyap\u0131s\u0131 \u00fczerinde \u00e7al\u0131\u015f\u0131r ve RESTful API’ler arac\u0131l\u0131\u011f\u0131yla eri\u015filebilir. Kullan\u0131c\u0131lar, metin verilerini Comprehend API’sine g\u00f6nderir ve hizmet, saniyeler i\u00e7inde analiz sonu\u00e7lar\u0131n\u0131 d\u00f6nd\u00fcr\u00fcr.<\/p>\n

API Entegrasyonu<\/h4>\n

Geli\u015ftiriciler, AWS SDK’lar\u0131n\u0131 (Python, Java, Node.js vb. i\u00e7in) kullanarak Comprehend API’sine kolayca entegre olabilirler. Metin verileri, API \u00e7a\u011fr\u0131lar\u0131n\u0131n bir par\u00e7as\u0131 olarak g\u00f6nderilir ve analiz sonu\u00e7lar\u0131 JSON format\u0131nda geri al\u0131n\u0131r. Bu, mevcut uygulamalara NLP yeteneklerini eklemeyi son derece basit hale getirir.<\/p>\n

Konsol \u00dczerinden Kullan\u0131m<\/h4>\n

AWS Y\u00f6netim Konsolu, Comprehend’i denemek ve k\u00fc\u00e7\u00fck \u00f6l\u00e7ekli analizler yapmak i\u00e7in kullan\u0131c\u0131 dostu bir aray\u00fcz sunar. Konsol \u00fczerinden metin girebilir, farkl\u0131 analiz t\u00fcrlerini se\u00e7ebilir ve sonu\u00e7lar\u0131 an\u0131nda g\u00f6rebilirsiniz. Bu, hizmetin yeteneklerini h\u0131zl\u0131ca ke\u015ffetmek i\u00e7in harika bir yoldur.<\/p>\n

Veri G\u00fcvenli\u011fi ve Gizlilik<\/h4>\n

AWS, Comprehend’e g\u00f6nderilen verilerin g\u00fcvenli\u011fini ve gizlili\u011fini en \u00fcst d\u00fczeyde tutar. Veriler aktar\u0131m s\u0131ras\u0131nda ve depolama s\u0131ras\u0131nda \u015fifrelenir. Comprehend, g\u00f6nderilen metin verilerini hizmeti iyile\u015ftirmek i\u00e7in kullanmaz, bu da hassas verilerle \u00e7al\u0131\u015fan i\u015fletmeler i\u00e7in \u00f6nemli bir g\u00fcvencedir. Ayr\u0131ca, AWS IAM (Identity and Access Management) ile eri\u015fim kontrol\u00fc sa\u011flanarak, kimlerin hangi verilere eri\u015febilece\u011fi ve hangi i\u015flemleri yapabilece\u011fi detayl\u0131 bir \u015fekilde y\u00f6netilebilir.<\/p>\n

Kullan\u0131m Senaryolar\u0131 ve Avantajlar\u0131<\/h3>\n

Amazon Comprehend, \u00e7ok \u00e7e\u015fitli sekt\u00f6rlerde ve i\u015f alanlar\u0131nda de\u011ferli uygulamalara sahiptir.<\/p>\n

M\u00fc\u015fteri Geri Bildirimlerinin Analizi<\/h4>\n

\u0130\u015fletmeler, m\u00fc\u015fteri yorumlar\u0131n\u0131, anket yan\u0131tlar\u0131n\u0131 ve destek taleplerini analiz ederek m\u00fc\u015fteri memnuniyetsizli\u011finin veya memnuniyetinin temel nedenlerini belirleyebilirler. Duygu analizi ve anahtar ifade \u00e7\u0131karma, belirli \u00fcr\u00fcn veya hizmet \u00f6zellikleriyle ilgili yayg\u0131n sorunlar\u0131 veya be\u011fenileri h\u0131zla tespit etmeye yard\u0131mc\u0131 olur.<\/p>\n

\u0130\u00e7erik Kategorizasyonu ve Etiketleme<\/h4>\n

Web siteleri, bloglar veya belge ar\u015fivleri gibi b\u00fcy\u00fck i\u00e7erik depolar\u0131, Comprehend ile otomatik olarak kategorize edilebilir ve etiketlenebilir. Bu, i\u00e7eri\u011fin daha kolay bulunmas\u0131n\u0131, organize edilmesini ve ki\u015fiselle\u015ftirilmi\u015f \u00f6neriler sunulmas\u0131n\u0131 sa\u011flar.<\/p>\n

Sosyal Medya \u0130zleme<\/h4>\n

Markalar, sosyal medya platformlar\u0131ndaki bahsedenleri ve yorumlar\u0131 Comprehend ile analiz ederek markalar\u0131 hakk\u0131ndaki genel duyarl\u0131l\u0131\u011f\u0131 izleyebilirler. Potansiyel krizleri erken tespit etmek veya pazarlama kampanyalar\u0131n\u0131n etkinli\u011fini \u00f6l\u00e7mek i\u00e7in kritik \u00f6neme sahiptir.<\/p>\n

Hukuki ve T\u0131bbi Metin Analizi<\/h4>\n

Hukuk firmalar\u0131 ve sa\u011fl\u0131k kurulu\u015flar\u0131, b\u00fcy\u00fck hacimli belgelerdeki (s\u00f6zle\u015fmeler, mahkeme kararlar\u0131, t\u0131bbi raporlar) \u00f6nemli bilgileri (tarihler, isimler, te\u015fhisler, ila\u00e7lar) otomatik olarak \u00e7\u0131karmak i\u00e7in Comprehend’i kullanabilirler. Bu, s\u00fcre\u00e7leri h\u0131zland\u0131r\u0131r ve insan hatas\u0131 riskini azalt\u0131r.<\/p>\n

Amazon Comprehend’i Kullanmaya Ba\u015flama<\/h3>\n

Amazon Comprehend’i kullanmaya ba\u015flamak olduk\u00e7a basittir. \u0130\u015fte temel ad\u0131mlar ve bir Python \u00f6rne\u011fi:<\/p>\n

AWS Hesab\u0131 Olu\u015fturma<\/h4>\n

\u0130lk ad\u0131m, bir AWS hesab\u0131 olu\u015fturmakt\u0131r. Yeni kullan\u0131c\u0131lar genellikle belirli bir s\u00fcre boyunca \u00fccretsiz katman avantajlar\u0131ndan yararlanabilirler.<\/p>\n

AWS CLI ve SDK Kurulumu<\/h4>\n

Uygulamalar\u0131n\u0131zdan Comprehend’e eri\u015fmek i\u00e7in AWS Komut Sat\u0131r\u0131 Arabirimi (CLI) veya tercih etti\u011finiz programlama diline ait AWS SDK’s\u0131n\u0131 kurman\u0131z gerekecektir. \u00d6rne\u011fin, Python i\u00e7in boto3<\/code> SDK’s\u0131 kullan\u0131l\u0131r.<\/p>\n

pip install boto3<\/pre>\n
<\/code><\/p>\n
Basit Bir Python \u00d6rne\u011fi<\/h4>\n
A\u015fa\u011f\u0131daki Python kodu, boto3<\/code> kullanarak bir metnin duygu analizini nas\u0131l yapaca\u011f\u0131n\u0131z\u0131 g\u00f6sterir:<\/p>\n
import boto3\n\ndef analyze_sentiment_comprehend(text):\n    \"\"\"\n    Amazon Comprehend kullanarak bir metnin duygu analizini yapar.\n    \"\"\"\n    client = boto3.client('comprehend', region_name='us-east-1') # B\u00f6lgenizi ayarlay\u0131n\n\n    try:\n        response = client.detect_sentiment(Text=text, LanguageCode='tr') # tr: T\u00fcrk\u00e7e i\u00e7in\n        sentiment = response['Sentiment']\n        sentiment_scores = response['SentimentScore']\n\n        print(f\"Metin: '{text}'\")\n        print(f\"Genel Duygu: {sentiment}\")\n        print(f\"Duygu Skorlar\u0131:\")\n        print(f\"  Olumlu: {sentiment_scores['Positive']:.2f}\")\n        print(f\"  Olumsuz: {sentiment_scores['Negative']:.2f}\")\n        print(f\"  N\u00f6tr: {sentiment_scores['Neutral']:.2f}\")\n        print(f\"  Kar\u0131\u015f\u0131k: {sentiment_scores['Mixed']:.2f}\")\n\n    except Exception as e:\n        print(f\"Hata olu\u015ftu: {e}\")\n\nif __name__ == \"__main__\":\n    sample_text_positive = \"Bu \u00fcr\u00fcn beklentilerimin \u00e7ok \u00fczerinde \u00e7\u0131kt\u0131, kesinlikle tavsiye ederim!\"\n    sample_text_negative = \"Hizmet kalitesi olduk\u00e7a k\u00f6t\u00fcyd\u00fc, bir daha asla kullanmam.\"\n    sample_text_neutral = \"Hava durumu bug\u00fcn par\u00e7al\u0131 bulutlu olacak.\"\n\n    analyze_sentiment_comprehend(sample_text_positive)\n    print(\"-\" * 30)\n    analyze_sentiment_comprehend(sample_text_negative)\n    print(\"-\" * 30)\n    analyze_sentiment_comprehend(sample_text_neutral)<\/pre>\n
<\/code>
\nBu kod, detect_sentiment<\/code> API'sini \u00e7a\u011f\u0131rarak metnin duygu analizini yapar ve sonu\u00e7lar\u0131 yazd\u0131r\u0131r. LanguageCode<\/code> parametresini tr<\/code> olarak ayarlayarak T\u00fcrk\u00e7e metinleri analiz edebilirsiniz.<\/p>\n
Geli\u015fmi\u015f \u00d6zellikler: \u00d6zel Varl\u0131klar ve S\u0131n\u0131fland\u0131rma<\/h3>\n
Comprehend, standart NLP yeteneklerinin \u00f6tesine ge\u00e7erek, i\u015fletmelerin kendi \u00f6zel ihtiya\u00e7lar\u0131na g\u00f6re modeller olu\u015fturmas\u0131na olanak tan\u0131yan geli\u015fmi\u015f \u00f6zellikler sunar.<\/p>\n
\u00d6zel Varl\u0131k Tan\u0131ma (Custom Entity Recognition)<\/h4>\n
Varsay\u0131lan varl\u0131k t\u00fcrleri (ki\u015fi, yer, kurulu\u015f vb.) baz\u0131 sekt\u00f6rler i\u00e7in yeterli olmayabilir. \u00d6rne\u011fin, bir ila\u00e7 \u015firketi belirli ila\u00e7 isimlerini veya hastal\u0131k adlar\u0131n\u0131 \u00f6zel varl\u0131klar olarak tan\u0131mak isteyebilir. \u00d6zel Varl\u0131k Tan\u0131ma ile kendi etiketlerinizi tan\u0131mlayabilir ve Comprehend'i bu etiketleri metinlerinizde tan\u0131mak \u00fczere e\u011fitebilirsiniz.<\/p>\n
\u00d6zel Metin S\u0131n\u0131fland\u0131rma (Custom Classification)<\/h4>\n
Comprehend'in \u00f6zel s\u0131n\u0131fland\u0131rma \u00f6zelli\u011fi, metinleri kendi tan\u0131mlad\u0131\u011f\u0131n\u0131z kategorilere g\u00f6re s\u0131n\u0131fland\u0131rman\u0131za olanak tan\u0131r. \u00d6rne\u011fin, bir e-ticaret sitesi m\u00fc\u015fteri e-postalar\u0131n\u0131 \"iade talebi\", \"teknik destek\", \"\u00fcr\u00fcn bilgisi\" gibi kategorilere ay\u0131rmak isteyebilir. Kendi etiketli veri setinizi sa\u011flayarak, Comprehend'i bu \u00f6zel kategorileri tan\u0131mak \u00fczere e\u011fitebilirsiniz.<\/p>\n
Model E\u011fitimi ve De\u011ferlendirme<\/h4>\n
\u00d6zel modeller olu\u015fturmak i\u00e7in, etiketlenmi\u015f bir veri setine ihtiyac\u0131n\u0131z vard\u0131r. Bu veri seti, Comprehend'e hangi metin par\u00e7alar\u0131n\u0131n hangi varl\u0131k t\u00fcr\u00fcne veya hangi kategoriye ait oldu\u011funu \u00f6\u011fretir. Comprehend, bu verileri kullanarak bir model e\u011fitir ve modelin performans\u0131n\u0131 de\u011ferlendirmenize olanak tan\u0131yan metrikler sunar. Model e\u011fitimi tamamen y\u00f6netilen bir s\u00fcre\u00e7tir, yani altyap\u0131 y\u00f6netimi konusunda endi\u015felenmenize gerek kalmaz.<\/p>\n
Fiyatland\u0131rma ve En \u0130yi Uygulamalar<\/h3>\n
Amazon Comprehend'in maliyet etkin bir \u015fekilde kullan\u0131lmas\u0131 i\u00e7in fiyatland\u0131rma modelini anlamak ve en iyi uygulamalar\u0131 takip etmek \u00f6nemlidir.<\/p>\n
Comprehend Fiyatland\u0131rma Modeli<\/h4>\n
Comprehend, kulland\u0131k\u00e7a \u00f6de (pay-as-you-go) modeliyle \u00e7al\u0131\u015f\u0131r. Fiyatland\u0131rma, i\u015flenen metin birimlerinin say\u0131s\u0131na ve kullan\u0131lan \u00f6zelli\u011fe g\u00f6re de\u011fi\u015fir. Genellikle, ilk birka\u00e7 milyon karakter veya birim \u00fccretsiz katman kapsam\u0131nda sunulur, bu da hizmeti denemek i\u00e7in harika bir f\u0131rsatt\u0131r. \u00d6zel modellerin e\u011fitimi ve bar\u0131nd\u0131r\u0131lmas\u0131 i\u00e7in de ayr\u0131 \u00fccretlendirmeler mevcuttur.<\/p>\n
Maliyet Optimizasyonu \u0130pu\u00e7lar\u0131<\/h4>\n
*   Toplu \u0130\u015fleme:<\/strong> B\u00fcy\u00fck metin bloklar\u0131n\u0131 tek tek g\u00f6ndermek yerine toplu olarak i\u015flemek genellikle daha uygun maliyetlidir.
\n*   Gereksiz \u00c7a\u011fr\u0131lardan Ka\u00e7\u0131nma:<\/strong> Yaln\u0131zca ihtiyac\u0131n\u0131z olan analiz t\u00fcrlerini \u00e7a\u011f\u0131r\u0131n. \u00d6rne\u011fin, sadece duygu analizi yapman\u0131z gerekiyorsa varl\u0131k tan\u0131ma \u00e7a\u011fr\u0131s\u0131 yapmay\u0131n.
\n*   \u00dccretsiz Katman\u0131 Kullanma:<\/strong> Yeni projeler veya denemeler i\u00e7in \u00fccretsiz katman limitlerini iyi de\u011ferlendirin.
\n*   Veri \u00d6n \u0130\u015fleme:<\/strong> Gereksiz bo\u015fluklar\u0131, \u00f6zel karakterleri veya \u00e7ok k\u0131sa, anlams\u0131z metinleri Comprehend'e g\u00f6ndermeden \u00f6nce temizleyin.<\/p>\n
En \u0130yi Kullan\u0131m Pratikleri<\/h4>\n
*   Do\u011fru Dil Kodu:<\/strong> Analiz etti\u011finiz metnin dil kodunu do\u011fru bir \u015fekilde belirtin (\u00f6rne\u011fin, tr<\/code> for Turkish).
\n*   Metin Boyutu:<\/strong> Comprehend'in API'leri belirli metin boyutu limitlerine sahiptir (genellikle 5 KB veya 5000 karakter). Daha uzun metinleri par\u00e7alara ay\u0131rarak g\u00f6ndermeniz gerekebilir.
\n*   Hata \u0130\u015fleme:<\/strong> Uygulamalar\u0131n\u0131zda Comprehend API \u00e7a\u011fr\u0131lar\u0131 i\u00e7in sa\u011flam hata i\u015fleme mekanizmalar\u0131 uygulay\u0131n.
\n*   Model G\u00fcncelleme:<\/strong> \u00d6zel modeller kullan\u0131yorsan\u0131z, zamanla verileriniz de\u011fi\u015ftik\u00e7e modellerinizi yeniden e\u011fitmeyi ve g\u00fcncellemeyi d\u00fc\u015f\u00fcn\u00fcn.<\/p>\n
Sonu\u00e7<\/h3>\n
Amazon Comprehend, i\u015fletmelerin metin verilerinden anlaml\u0131 i\u00e7g\u00f6r\u00fcler elde etmelerini sa\u011flayan g\u00fc\u00e7l\u00fc ve eri\u015filebilir bir yapay zeka hizmetidir. Do\u011fal dil i\u015fleme yeteneklerini kodlama veya makine \u00f6\u011frenimi uzmanl\u0131\u011f\u0131 gerektirmeden sunarak, m\u00fc\u015fteri geri bildirimlerini analiz etmekten i\u00e7erik y\u00f6netimine kadar bir\u00e7ok alanda devrim yaratmaktad\u0131r. Geli\u015fmi\u015f \u00f6zel modelleme yetenekleriyle birlikte, Comprehend, her b\u00fcy\u00fckl\u00fckteki kurulu\u015fun veri odakl\u0131 kararlar almas\u0131na ve rekabet avantaj\u0131 sa\u011flamas\u0131na yard\u0131mc\u0131 olan vazge\u00e7ilmez bir ara\u00e7t\u0131r. AWS'in geni\u015f yapay zeka ekosistemi i\u00e7inde yer alan Comprehend, metin anlama yeteneklerinizi bir sonraki seviyeye ta\u015f\u0131man\u0131z i\u00e7in gereken t\u00fcm ara\u00e7lar\u0131 sunar.<\/p>\n
S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h3>\n
1. Amazon Comprehend hangi dilleri destekler?<\/h4>\n
Amazon Comprehend, \u0130ngilizce, \u0130spanyolca, Almanca, Frans\u0131zca, \u0130talyanca, Portekizce, Japonca, Korece, \u00c7ince (Basitle\u015ftirilmi\u015f), Arap\u00e7a, Rus\u00e7a ve T\u00fcrk\u00e7e dahil olmak \u00fczere bir\u00e7ok farkl\u0131 dili desteklemektedir. Desteklenen dillerin tam listesi ve her \u00f6zelli\u011fin dil deste\u011fi i\u00e7in AWS belgelerine bakman\u0131z \u00f6nerilir.<\/p>\n
2. Amazon Comprehend'i kullanmak i\u00e7in programlama bilgisi gerekli mi?<\/h4>\n
Temel API \u00e7a\u011fr\u0131lar\u0131 i\u00e7in programlama bilgisi (\u00f6rne\u011fin Python ile boto3<\/code> kullan\u0131m\u0131) gereklidir. Ancak, AWS Y\u00f6netim Konsolu \u00fczerinden hizmeti deneyebilir ve basit metin analizlerini programlama bilgisi olmadan ger\u00e7ekle\u015ftirebilirsiniz.<\/p>\n
3. Verilerim Amazon Comprehend taraf\u0131ndan nas\u0131l korunur?<\/h4>\n
AWS, Comprehend'e g\u00f6nderilen verilerin g\u00fcvenli\u011fini ve gizlili\u011fini ciddiye al\u0131r. Veriler aktar\u0131m s\u0131ras\u0131nda ve depolama s\u0131ras\u0131nda \u015fifrelenir. AWS, hizmeti iyile\u015ftirmek i\u00e7in m\u00fc\u015fteri verilerini kullanmaz. Ayr\u0131ca, AWS IAM ile kimlik ve eri\u015fim y\u00f6netimi yap\u0131larak verilere eri\u015fim kontrol alt\u0131nda tutulur.<\/p>\n
4. Amazon Comprehend ile di\u011fer AWS hizmetleri entegre edilebilir mi?<\/h4>\n
Evet, Amazon Comprehend di\u011fer AWS hizmetleriyle sorunsuz bir \u015fekilde entegre edilebilir. \u00d6rne\u011fin, Amazon S3'te depolanan metin dosyalar\u0131n\u0131 i\u015flemek, Amazon Kinesis ile ger\u00e7ek zamanl\u0131 veri ak\u0131\u015flar\u0131n\u0131 analiz etmek veya AWS Lambda fonksiyonlar\u0131 arac\u0131l\u0131\u011f\u0131yla tetiklenen olaylara yan\u0131t vermek i\u00e7in kullan\u0131labilir.<\/p>\n
5. Amazon Comprehend'in \u00fccretsiz katman\u0131 var m\u0131?<\/h4>\n
Evet, Amazon Comprehend'in bir \u00fccretsiz katman\u0131 mevcuttur. Bu katman, belirli bir s\u00fcre boyunca (genellikle 12 ay) belirli bir miktar metin i\u015fleme ve analizini \u00fccretsiz olarak yapman\u0131za olanak tan\u0131r. \u00dccretsiz katman limitleri ve detaylar\u0131 i\u00e7in AWS fiyatland\u0131rma sayfas\u0131na g\u00f6z atman\u0131z \u00f6nemlidir.<\/p>\n","protected":false},"excerpt":{"rendered":"Amazon Comprehend ile Metin Anlama: AWS Yapay Zeka Serisi\nG\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda, her ge\u00e7en g\u00fcn artan devasa metin verileri y\u0131\u011f\u0131n\u0131, i\u015fletmeler i\u00e7in…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1406],"tags":[],"class_list":{"0":"post-38142","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-aws","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nAmazon Comprehend ile Metin Anlama: AWS Yapay Zeka Serisi - Kodlar\u0131n Gizemli D\u00fcnyas\u0131<\/title>\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\/amazon-comprehend-ile-metin-anlama-aws-yapay-zeka-serisi\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Amazon Comprehend ile Metin Anlama: AWS Yapay Zeka Serisi\" \/>\n<meta property=\"og:description\" content=\"Amazon Comprehend ile Metin Anlama: AWS Yapay Zeka Serisi G\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda, her ge\u00e7en g\u00fcn artan devasa metin verileri y\u0131\u011f\u0131n\u0131, i\u015fletmeler i\u00e7in...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/amazon-comprehend-ile-metin-anlama-aws-yapay-zeka-serisi\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2026-01-24T06:00:39+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"12 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/amazon-comprehend-ile-metin-anlama-aws-yapay-zeka-serisi\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/amazon-comprehend-ile-metin-anlama-aws-yapay-zeka-serisi\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Amazon Comprehend ile Metin Anlama: AWS Yapay Zeka Serisi\",\"datePublished\":\"2026-01-24T06:00:39+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/amazon-comprehend-ile-metin-anlama-aws-yapay-zeka-serisi\/\"},\"wordCount\":2300,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"articleSection\":[\"AWS\"],\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/amazon-comprehend-ile-metin-anlama-aws-yapay-zeka-serisi\/#respond\"]}],\"copyrightYear\":\"2026\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/amazon-comprehend-ile-metin-anlama-aws-yapay-zeka-serisi\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/amazon-comprehend-ile-metin-anlama-aws-yapay-zeka-serisi\/\",\"name\":\"Amazon Comprehend ile Metin Anlama: AWS Yapay Zeka Serisi - 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