IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network

Deepak Gupta, Pabitra Lenka, Harsimran Bedi, Asif Ekbal, Pushpak Bhattacharyya


Abstract
Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers. Therefore an automated system which can analyze the customer behavior is in great demand. Users may write feedbacks in any language, and hence mining appropriate information often becomes intractable. Especially in a traditional feature-based supervised model, it is difficult to build a generic system as one has to understand the concerned language for finding the relevant features. In order to overcome this, we propose deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches that do not require handcrafting of features. We evaluate these techniques for analyzing customer feedback sentences on four languages, namely English, French, Japanese and Spanish. Our empirical analysis shows that our models perform well in all the four languages on the setups of IJCNLP Shared Task on Customer Feedback Analysis. Our model achieved the second rank in French, with an accuracy of 71.75% and third ranks for all the other languages.
Anthology ID:
I17-4031
Volume:
Proceedings of the IJCNLP 2017, Shared Tasks
Month:
December
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
184–193
Language:
URL:
https://aclanthology.org/I17-4031
DOI:
Bibkey:
Cite (ACL):
Deepak Gupta, Pabitra Lenka, Harsimran Bedi, Asif Ekbal, and Pushpak Bhattacharyya. 2017. IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network. In Proceedings of the IJCNLP 2017, Shared Tasks, pages 184–193, Taipei, Taiwan. Asian Federation of Natural Language Processing.
Cite (Informal):
IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network (Gupta et al., IJCNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/I17-4031.pdf