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      Çağrı merkezi metin madenciliği yaklaşımı

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      Author
      Yiğit, İ. O.
      Ateş, A. F.
      Güvercin, Mehmet
      Ferhatosmanoğlu, Hakan
      Gedik, Buğra
      Date
      2017-05
      Source Title
      25th Signal Processing and Communications Applications Conference, SIU 2017
      Publisher
      IEEE
      Pages
      [1] - [4]
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      Günümüzde çağrı merkezlerindeki görüşme kayıtlarının sesten metne dönüştürülebilmesi görüşme kaydı metinleri üzerinde metin madenciliği yöntemlerinin uygulanmasını mümkün kılmaktadır. Bu çalışma kapsamında görüşme kaydı metinleri kullanarak görüşmenin içeriğinin duygu yönünden (olumlu/olumsuz) değerlendirilmesi, müşteri memnuniyetinin ve müşteri temsilcisi performansının ölçülmesi amaçlanmaktadır. Yapılan çalışmada görüşme kaydı metinlerinden metin madenciliği yöntemleri ile yeni özellikler çıkarılmıştır. Metinlerden elde edilen özelliklerden yararlanılarak sınıflandırma ve regresyon yöntemleriyle görüşme kayıtlarının içeriklerinin değerlendirilmesini sağlayacak tahmin modelleri oluşturulmuştur. Bu çalışma sonucunda ortaya çıkarılan tahmin modellerinin Türk Telekom bünyesindeki çağrı merkezlerinde kullanılması hedeflenmektedir.
       
      Nowadays, the ability to convert call records from voice to text makes it possible to apply text mining methods to extract information from calls. In this study, it is aimed not only to evaluate the sentiment (positive/negative) of the calls in general, but also to measure the customer satisfaction and representative's performance by using call record texts. New features have been extracted from texts using text mining methods. Using the features extracted, prediction models were developed to evaluate the contents of call records by classification and regression methods. As a result of this study, it is planned to utilize the prediction models developed in Turk Telekom's call centers. © 2017 IEEE.
      Keywords
      Classification
      Machine Learning
      Prediction
      Regression
      Supervised machine learning
      Artificial intelligence
      Classification (of information)
      Customer satisfaction
      Education
      Forecasting
      Learning systems
      Regression analysis
      Signal processing
      Text processing
      Call centers
      Extract informations
      Positive/negative
      Prediction model
      Regression
      Regression method
      Supervised machine learning
      Text mining
      Data mining
      Permalink
      http://hdl.handle.net/11693/37605
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2017.7960138
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      • Department of Computer Engineering 1308

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