Herkesin bir fikri var. Ancak makineler ayn\u0131 g\u00f6r\u00fc\u015flere sahip olamazlar. Makineler insanlar\u0131n fikirlerini yorumlamay\u0131 nas\u0131l \u00f6\u011frenebilir? Bu neden \u00f6nemli? Bunlar, bug\u00fcn\u00fcn duygu analizi blogunda ele alaca\u011f\u0131m\u0131z sorulard\u0131r (bir altk\u00fcme) do\u011fal dil i\u015fleme<\/a> (NLP).<\/p>\n Duygu analizi (g\u00f6r\u00fc\u015f madencili\u011fi olarak da bilinir), ses ve metinden s\u00fcbjektif bilgileri \u00e7\u0131karmak i\u00e7in kullan\u0131lan bir y\u00f6ntemdir. Buna \u00e7evrimi\u00e7i incelemeler, m\u00fc\u015fteri destek talepleri ve \u00e7evrimi\u00e7i incelemeler dahildir. Duygu analizi, en temel haliyle, s\u00fcbjektif bilginin olumlu, tarafs\u0131z veya olumsuz olup olmad\u0131\u011f\u0131n\u0131 belirler. Fakat, makine \u00f6\u011frenme<\/a> markalar\u0131n duygular\u0131 tan\u0131mlama ve daha az yayg\u0131n dil kullan\u0131mlar\u0131n\u0131 anlama veya \u00e7evrimi\u00e7i davran\u0131\u015flar\u0131 izleme gibi daha zor durumlarda duyarl\u0131l\u0131k analizini kullanmas\u0131na izin verdi.<\/p>\n Amazon ve di\u011fer \u00e7evrimi\u00e7i perakendeciler, t\u00fcketici tercihlerini tahmin etmek i\u00e7in duyarl\u0131l\u0131k analizini kullanan geli\u015fmi\u015f \u00f6neri motorlar\u0131 kullan\u0131r. Bu geli\u015fmi\u015f sistemler, bir \u00fcr\u00fcn\u00fcn ne kadar pop\u00fcler oldu\u011funu ve nedenini belirlemek i\u00e7in \u00fcr\u00fcn derecelendirmelerinden daha fazlas\u0131n\u0131 kullan\u0131r.<\/p>\n Markalar, m\u00fc\u015fteri destek biletlerine \u00f6ncelik vermek ve en etkili ileti\u015fim kanallar\u0131n\u0131 belirlemek i\u00e7in duygu analizini de kullanabilir. Bu bilgi, \u00fcr\u00fcn iyile\u015ftirmelerini planlamak i\u00e7in kullan\u0131labilir. Bu i\u00e7g\u00f6r\u00fcler, daha iyi m\u00fc\u015fteri deneyimleri ve yeni f\u0131rsatlar yaratman\u0131za yard\u0131mc\u0131 olabilir ve bu da karl\u0131l\u0131\u011f\u0131n\u0131z\u0131 art\u0131r\u0131r.<\/p>\n Sosyal medyada mevcut olan bilgi bollu\u011fu nedeniyle h\u00fck\u00fcmetler \u015feffafl\u0131\u011f\u0131 art\u0131rmak, vatanda\u015f kat\u0131l\u0131m\u0131n\u0131 te\u015fvik etmek ve insanlar\u0131n COVID-19'a nas\u0131l tepki verdi\u011fini anlamak i\u00e7in duygu analizini kullanmaya ba\u015flad\u0131. Bir duygu g\u00f6r\u00fc\u015f\u00fc, h\u00fck\u00fcmetlerin ve politika yap\u0131c\u0131lar\u0131n ortak toplumsal ve epidemiyolojik sorunlar\u0131 kontrolden \u00e7\u0131kmadan \u00f6nce belirlemelerine yard\u0131mc\u0131 olur.<\/p>\n Duygu analizi s\u00fcreci nedir?<\/p>\n Do\u011fal dil i\u015fleme, duygu analizinin modern y\u00f6ntemidir. Bu, bilgisayar bilimi ve insan dili aras\u0131nda bir aray\u00fcz olu\u015fturur. Bu arabirim, makinelerin metni anlamas\u0131n\u0131 ve ses dinlemesini sa\u011flar. Ayr\u0131ca, makinelerin basit derecelendirmelerin \u00f6tesinde say\u0131sal bilgiler sa\u011flamas\u0131na olanak tan\u0131r.<\/p>\n NLP, yaz\u0131l\u0131 ve s\u00f6zl\u00fc i\u00e7eri\u011fin ard\u0131ndaki ger\u00e7ek anlam\u0131 g\u00f6rmemizi sa\u011flar. Bu, duygu analizi yapman\u0131n yeni bir yoludur. Makineler art\u0131k verilerden \u00f6\u011frenebilir ve olumlu, tarafs\u0131z ve olumsuz ifadeleri alg\u0131layabilir. Bu, markalar\u0131n kapsaml\u0131 duygusal profiller olu\u015fturmas\u0131na olanak tan\u0131r. Sistemler ayr\u0131ca duygular\u0131 c\u00fcmle d\u00fczeyinde tan\u0131mlayabilir ve i\u015fleyebilir. Bu daha kesin bir yakla\u015f\u0131mla m\u00fcmk\u00fcnd\u00fcr.<\/p>\n Ama bir sorun var. Bu model, \u00e7ok say\u0131da ba\u011flamsal olarak a\u00e7\u0131klamal\u0131 e\u011fitim verisi gerektirir.<\/p>\n Yapay zeka zor bir analiz alan\u0131d\u0131r ve duygu analizi bunun nedenlerinden biridir. \u0130nsanlar bile, \u00f6zellikle belirsiz kelimeler, argo ve mecazlarla ilgili duygular\u0131 do\u011fru bir \u015fekilde anlamakta zorlan\u0131yor.<\/p>\n \u00d6znellik ba\u015fka bir sorundur. Di\u011fer bir zorluk ise \u00f6znelliktir. Alayc\u0131 s\u00f6zler s\u00f6ylemek i\u00e7in de kullan\u0131labilir. Boyutu ve rengi tan\u0131mlayan s\u0131fatlar\u0131 d\u00fc\u015f\u00fcn\u00fcn. Bir \u00fcr\u00fcn\u00fcn rengi, be\u011fendi\u011fi i\u00e7in veya bir noktay\u0131 kan\u0131tlamak i\u00e7in k\u0131rm\u0131z\u0131d\u0131r denilebilir. Makine, ba\u011flam\u0131 ve fark\u0131 anlama amac\u0131n\u0131 anlamal\u0131d\u0131r.<\/p>\n \u0130nsanlar \u00fcst\u00fc kapal\u0131 beyanlarda bulundu\u011fundan, ba\u011flam \u00f6nemlidir. Bir makine, a\u00e7\u0131k\u00e7a belirtilmemi\u015fse ba\u011flam\u0131 \u00f6\u011frenemez. "\u00dcr\u00fcn\u00fcm\u00fcz\u00fcn nesini be\u011fendiniz?" sorular\u0131n\u0131 yan\u0131tlay\u0131n. veya "Neyi be\u011fenmedin?" Soruya ba\u011fl\u0131 olarak, \u201chi\u00e7bir \u015fey\u201d veya \u201cher \u015fey\u201d gibi cevaplar, her biri duygunun kutuplar\u0131n\u0131 de\u011fi\u015ftirecektir.<\/p>\n Makinelerin harika bir mizah anlay\u0131\u015f\u0131 olmad\u0131\u011f\u0131 i\u00e7in duygu analizi de zor olabilir. Ancak makine, birinin ironi veya i\u011fneleme kulland\u0131\u011f\u0131n\u0131 alg\u0131layamazsa, utan\u00e7 verici yanl\u0131\u015f yorumlara neden olabilir.<\/p>\n Duyarl\u0131l\u0131k analizi modellerinin geli\u015ftirilmesindeki zorluklar bunlarla s\u0131n\u0131rl\u0131 de\u011fildir. Kar\u015f\u0131la\u015ft\u0131rmal\u0131 ifadelerin anlam\u0131n\u0131 tan\u0131mak ve anlamak ve tarafs\u0131zl\u0131k i\u00e7in bir temel olu\u015fturmak \u00f6nemlidir.<\/p>\n Bu zorluklar, duyarl\u0131l\u0131k analizi modelleri geli\u015ftirirken insanlar\u0131 bilgilendirmenin (HITL) \u00f6nemini vurgulamaktad\u0131r. Yaln\u0131zca insanlar duygular\u0131 deneyimleyebilir ve bu nedenle yaln\u0131zca onlar uygulanabilir bir model yaratabilir.<\/p>\n Bir duyarl\u0131l\u0131k analizi algoritmas\u0131 olu\u015fturmak i\u00e7in b\u00fcy\u00fck miktarda etiketlenmi\u015f veri gerekir. Duyarl\u0131l\u0131k analizi modellerinin geli\u015ftiricileri, bir veri haz\u0131rlama ekibi se\u00e7erken ba\u011flam\u0131 ve kalite g\u00fcvencesini dikkate almal\u0131d\u0131r. Bu \u00e7al\u0131\u015fma, saat ba\u015f\u0131na \u00fccret alan noterlerin, verileri do\u011fru bir \u015fekilde etiketleme ve haz\u0131rlama konusunda g\u00f6rev taraf\u0131ndan \u00f6denen ki\u015filere g\u00f6re daha muhtemel oldu\u011funu g\u00f6stermektedir. \u00d6te yandan, kitle kaynakl\u0131 \u00e7al\u0131\u015fanlar ve esnek \u00e7al\u0131\u015fanlar\u0131n duygular\u0131 yanl\u0131\u015f yorumlamalar\u0131 veya g\u00f6revi bitirmek i\u00e7in 'di\u011fer' se\u00e7ene\u011fini varsay\u0131lan olarak se\u00e7meleri daha olas\u0131d\u0131r.<\/p>\n Her iki d\u00fcnyan\u0131n da en iyisi: y\u00f6netilen i\u015f g\u00fcc\u00fc. Y\u00f6netilen bir i\u015f g\u00fcc\u00fc, incelenmi\u015f ve do\u011frudan sizin g\u00f6zetiminiz alt\u0131nda olan bir ekibe sahip olman\u0131n avantaj\u0131n\u0131 sunar. Bu, daha iyi kalite kontrol\u00fc ve proje hedefleriyle uyum sa\u011flar. D\u0131\u015f kaynakl\u0131 bir model olan y\u00f6netilen i\u015f g\u00fcc\u00fc, kitle kaynak kullan\u0131m\u0131 veya esnek \u00e7al\u0131\u015fanlarla ekip \u00e7al\u0131\u015fmas\u0131 taraf\u0131ndan sunulanlara rakip olan bir d\u00fczeyde esneklik ve \u00f6l\u00e7eklenebilirlik sunar.<\/p>\n Her \u015fey do\u011fru i\u015f g\u00fcc\u00fcn\u00fc se\u00e7mekten ge\u00e7iyor. Verilerinizi \u00f6nemseyen ve s\u00fcrekli e\u011fitim alan. Veri etiketleme sanat oldu\u011fu kadar bilimsel de olabilir. Veri etiketlemenin hem teknik hem de insani y\u00f6nleri hakk\u0131nda bilgi sahibi ki\u015filerle i\u015fbirli\u011fi yapmay\u0131 d\u00fc\u015f\u00fcn\u00fcn. Modelleriniz ve tahminleriniz i\u00e7in \u00e7ok \u00f6nemlidir.<\/p>","protected":false},"excerpt":{"rendered":" Sentiment Analysis – and Why Computers Can’t Do It Alone Everybody has an opinion. But machines are not able to have the same opinions. How can machines learn to interpret people’s opinions? Why is this important? These are the questions that we will address in today’s blog on sentiment analysis (a subset of natural-language processing […]<\/p>","protected":false},"author":3,"featured_media":14323,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3042","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-annotation"],"blocksy_meta":[],"featured_image_urls":{"full":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis.jpg",2240,1260,false],"thumbnail":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-150x150.jpg",150,150,true],"medium":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-300x169.jpg",300,169,true],"medium_large":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-768x432.jpg",768,432,true],"large":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-1024x576.jpg",1024,576,true],"1536x1536":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-1536x864.jpg",1536,864,true],"2048x2048":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-2048x1152.jpg",2048,1152,true],"trp-custom-language-flag":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-18x10.jpg",18,10,true],"ultp_layout_landscape_large":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-1200x800.jpg",1200,800,true],"ultp_layout_landscape":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-870x570.jpg",870,570,true],"ultp_layout_portrait":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-600x900.jpg",600,900,true],"ultp_layout_square":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-600x600.jpg",600,600,true],"yarpp-thumbnail":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/12\/Sentiment-Analysis-120x120.jpg",120,120,true]},"post_excerpt_stackable":" Sentiment Analysis – and Why Computers Can’t Do It Alone Everybody has an opinion. But machines are not able to have the same opinions. How can machines learn to interpret people’s opinions? Why is this important? These are the questions that we will address in today’s blog on sentiment analysis (a subset of natural-language processing (NLP). What is sentiment analysis and why does it matter to you? Sentiment analysis (also known as opinion mining) is a method for extracting subjective information from audio and text. This includes online reviews, customer support requests, and online reviews. Sentiment analysis, in its most…<\/p>\n","category_list":"Data Annotation<\/a>","author_info":{"name":"Parth P","url":"https:\/\/www.datalabelify.com\/tr\/author\/soeuidhae\/"},"comments_num":"0 comments","_links":{"self":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts\/3042","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/comments?post=3042"}],"version-history":[{"count":3,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts\/3042\/revisions"}],"predecessor-version":[{"id":3077,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts\/3042\/revisions\/3077"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/media\/14323"}],"wp:attachment":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/media?parent=3042"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/categories?post=3042"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/tags?post=3042"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Duygu analizi nedir ve sizin i\u00e7in neden \u00f6nemli?<\/h3>\n
Duygu analizi neden bu kadar zor?<\/h4>\n
Duygu analizi e\u011fitimine en etkili \u015fekilde nas\u0131l yakla\u015fabilirsiniz?<\/h3>\n