Complaint Classification using Word2Vec Model

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Attempt has been made to develop a versatile, universal complaint grievance segregator by classifying orally acknowledged grievances

    into one of the predefined categories. The oral complaints are first converted to text and then each word is represented by a vector using

    word2vec. Each grievance is represented by a single vector using Gated Recurrent Unit (GRU) that implements the hidden state of Recurrent Neural Network (RNN) model. The popular Multi-Layer Perceptron (MLP) has been used as the classifier to identify the categories.

     


  • Keywords


    Gated Recurrent Unit; Recurrent Neural Network; Text Classification; Word2Vec

  • References


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Article ID: 20192
 
DOI: 10.14419/ijet.v7i4.5.20192




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