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prediction-review-rating

Subtitle : Text Classification using RNN and CNN with keras and tensorflow

Description

Based on product reviews, we want to see how a review relates to ratings. In this project, a big topic called Text classification is implemented using two models. First, we implemented the LSTM model using Keras and train the binary classification. And the CNN model was implemented using Tensorflow to train and test multiple classification. We tested the post-learning using LSTM, which is a type of RNN known as a representative text processing model. Next, we implemented multiple text classification using CNN, which is mainly used for image processing. In conclusion, we have tested the possibility of how much performance can be expressed by text classification using RNN and CNN, which are representative algorithms of deep learning, and how much of the learning result, in this project . Especially, in order to process texts of CNN that performs image processing, the order of appearance of words and expressions is reflected in learning by preserving local information of sentences.

Requirements

  • Flask for web server
  • Jupyter for running python program and leraning rnn/cnn
  • tensorflow
  • keras

How to use our program

Run the Web Application

$ python3 web.py

Must Change!

  1. In app/model/checkout, change a path
  2. paths of dataset

Learn model

If you get dataset and model ipynb files and change the path of dataset, you can learn cnn or rnn model using dataset.

Software(or Overall Application) Architecture

Architecture

Process and Result of CNN and RNN

CNN

  1. Step1 cnn_step1
  2. Step2 cnn_step2
  3. Step3 cnn_step3
  4. Result cnn_result

RNN

  1. Step1 rnn_step1
  2. Step2 rnn_step2
  3. Result rnn_result

References

Team Member

  • Dokyeong Kwon
  • Seungwoo Park
  • Taeseung Lee

SlideShare Note

https://www.slideshare.net/TaeseungLee1/prediction-of-amazon-review-rating-by-using-amazon-review

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