Recurrent Neural Network with Word Embedding for Complaint Classification

Panuwat Assawinjaipetch, Kiyoaki Shirai, Virach Sornlertlamvanich, Sanparith Marukata


Abstract
Complaint classification aims at using information to deliver greater insights to enhance user experience after purchasing the products or services. Categorized information can help us quickly collect emerging problems in order to provide a support needed. Indeed, the response to the complaint without the delay will grant users highest satisfaction. In this paper, we aim to deliver a novel approach which can clarify the complaints precisely with the aim to classify each complaint into nine predefined classes i.e. acces-sibility, company brand, competitors, facilities, process, product feature, staff quality, timing respec-tively and others. Given the idea that one word usually conveys ambiguity and it has to be interpreted by its context, the word embedding technique is used to provide word features while applying deep learning techniques for classifying a type of complaints. The dataset we use contains 8,439 complaints of one company.
Anthology ID:
W16-5205
Volume:
Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies (WLSI/OIAF4HLT2016)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
OIAF4HLT | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
36–43
Language:
URL:
https://www.aclweb.org/anthology/W16-5205
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-5205.pdf