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README.md

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.

Citing the paper

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


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

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

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

Reporting Doubts and Errors

  • For any queries, please drop me an email at pabitra.lenka18@gmail.com.
  • 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.

About

Multi Class Text (Feedback) Classification using CNN, GRU Network and pre trained Word2Vec embedding, word embeddings on TensorFlow.

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