Customer Feedback Analysis, IJCNLP'17

• Our goal is to determine what class(es) the customer feedback sentences should be annotated with five-plus-one-classes categorization (comment, request, bug, complaint, meaningless and undetermined) as in four languages i.e. English, French, Japanese and Spanish.
• This is one of the shared tasks of IJCNLP - 2017. For more details about the task, please visit here.

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Citing the paper

If you are using this code for any sort of research, please cite our paper

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

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Dataset

Training Data samples for CNN (training.tsv) from different languages used

tag	consumer_complaint_narrative
comment	Rooms and sitting area was always immaculate.
request	:) Deberían abrir vacantes para beta-testers :)
meaningless	il beug tou le temp
complaint	シャンプーが泡立たない

Test Data samples for CNN (test.tsv) from different languages used

id	consumer_complaint_narrative
en-test-0002	You can't go wrong!!!
es-test-0004	La habitación súper grande! muy cómoda..
fr-test-0006	La salle de bains est splendide.
jp-test-0016	日々の忙しさを忘れて、娘が優しくされると優しくなれるね

Training Data samples for CNN + RNN (training.tsv) from different languages used

Category	Descript
comment	Rooms and sitting area was always immaculate.
request	:) Deberían abrir vacantes para beta-testers :)
meaningless	il beug tou le temp
complaint	シャンプーが泡立たない

Test Data samples for CNN + RNN (test.tsv) from different languages used

id	Descript
en-test-0002	You can't go wrong!!!
es-test-0004	La habitación súper grande! muy cómoda..
fr-test-0006	La salle de bains est splendide.
jp-test-0016	日々の忙しさを忘れて、娘が優しくされると優しくなれるね

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Running the Code

For CNN

Train

• Command : python3 train.py training.tsv parameters.json
• A directory will be created during training, and the best model will be saved in this directory.

Test

• Provide the model directory (created when running train.py) and test data to predict.py
• Command : python3 predict.py trained_model_1505467324/ test.tsv

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For CNN + RNN

Train

• Command : python3 train.py training.tsv training_config.json
• A directory will be created during training, and the best model will be saved in this directory.

Test

• Provide the model directory (created when running train.py) and test data to predict.py
• Command : python3 predict.py trained_results_1505468375/ test.tsv

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Reporting Doubts and Errors

• For any queries, please drop me an email at [email protected].
• Please refer to the publication for detailed results and model performances.

Credits

• I would like to thank Jie Zhang and Denny Britz for sharing their code.
• We have used their code and modified according to our need by incorporating pre-trained Word2Vec embedding.
• Deepak Gupta has also contributed to this code repository.