Open AccessCOMPARISON INTENT RECOGNITION ON FOOD DELIVERY SERVICE COMPLAINT IN TWITTER WITH RECURRENT AND CONVOLUTIONAL NEURAL NETWORK
Author(s) -
Irfan Nasrullah,
Rila Mandala
Publication year - 2020
Publication title -
it for society
Language(s) - English
Resource type - Journals
ISSN - 2527-595X
DOI - 10.33021/itfs.v5i1.1203
Subject(s) - computer science , vectorization (mathematics) , complaint , convolutional neural network , artificial intelligence , domain (mathematical analysis) , natural language processing , conversation , key (lock) , service (business) , word (group theory) , relation (database) , pattern recognition (psychology) , data mining , mathematics , mathematical analysis , linguistics , philosophy , geometry , computer security , economy , parallel computing , political science , law , economics
In this research, the case of intent classification for Customer Relation Management (CRM) how to handle complaints as a domain to be followed up, where datasets are extracted from the conversation on Twitter. The research objectives support three key findings to comparing the CNNs and BRNNs model to intent recognition by vectorization text: (1) Which architecture performs better (accuracy) depends on how important it is to semantically understand the whole sequence and (2) Learning rate changes performance relatively smoothly, while the optimal result iterated by change hidden size and batch size result in large fluctuations. (3) Last, how word vectorization is able to define sub-domain of the complaints by word vector classification.