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Gated Reccurrent Unit Application in Sentiment Analysis

Implemention for Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Read the paper here.

Compare and evaluate recurrent neural networks (RNN) with different types of recurrent units:

  • Gated recurrent unit (GRU)
  • Long short-term memory (LSTM)
  • Hyperbolic tangent (tanh)

We also implement our project in Vietnamese.

Architecture Used

Advisor: Ngoc Nguyen

Authors: Duong Tran, Dung Nguyen, Sang Nguyen

I. Setup Environment

  • Step 1: Create the environment (Make sure you have installed the lastest Miniconda)
conda env create -f environment.yml
  • Step 2: Activate the environment
conda activate gru

II. Set up your dataset

  • Your dataset must be a .csv file.

  • You should remember the directory where you put the dataset in order to use it for training.

    • The default directory is ../data/...
    • For example: ../data/IMDB_Dataset.csv.

  • You can use the default IMDB_Dataset.csv or IMDB_Dataset_mini.csv which are available in ../data.

  • More about the IMDB_Dataset at Kaggle.

  • If you use another dataset, remember that your dataset should be same as this architecture.

data_name data_label
Training sentence 1 label 1
Trainning sentence 2 label 2
... ...
  • For example: IMDB_Dataset.csv
review sentiment
What a disappointment! Piper Perabo... negative
I love this movie. It was one of my favorite m... positive
... ...

III. Training Process

Review training on colab

Training script:

!python train.py --epochs ${epochs} 
                --model-folder ${model_folder}
                --checkpoint-folder ${checkpoint_folder}
                --data-path ${data_path}
                --data-name ${data_name}
                --label-name ${label_name}
                --data-classes ${data_classes}
                --num-class ${num_classes}
                --model ${model} 
                --units ${units}
                --embedding-size ${embedding_size}
                --vocab-size ${vocab_size}
                --max-length ${max_length}
                --learning-rate ${learning_rate}
                --optimizer ${optimizer}
                --test-size ${test_size}
                --batch-size ${batch_size}
                --buffer-size ${buffer_size}

Example for IMDB dataset:

!python train.py --epochs 20 --model gru --optimizer rmsprop --units 128 --embedding-size 128 --vocab-size=10000 --max-length 256 --learning-rate 0.0008  --test-size 0.2 --batch-size 32 --buffer-size 128

Example for others dataset:

!python train.py --epochs 20 --model gru --learning-rate 0.0008 --optimizer rmsprop --model-folder /tmp/model/ --checkpoint-folder /tmp/checkpoints/ --data-path data/IMDB_Dataset.csv --data-name review  --label-name sentiment --data-classes {'negative': 0, 'positive': 1} --num-class 2 --units 128 --embedding-size 128 --vocab-size=10000 --max-length 256  --test-size 0.2 --batch-size 32 --buffer-size 128

There are some important arguments for the script you should consider when running it:

  • model-folder: Directory model. (E.g. tmp/model)
  • checkpoint-folder: Directory checkpoints. (E.g. tmp/checkpoints)
  • data-path: The path of dataset (Must be csv format. E.g. data/IMDB_Dataset.csv)
  • data-name: The folder of validation data
  • data-name: Name of data column that having sentences will be train. (e.g. review)
  • label-name: Name of label column that having labels will be train. (e.g. sentiment )
  • data-classes: Set of labels that you need to convert to categorical. (E.g. {'negative': 0, 'positive': 1})
  • num-class: Number of labels in your dataset. (e.g. 2 labels)
  • model: Choose one model that you want to test including: gru, lstm, tanh. Default is gru
  • units: Hidden dimension. Default is 128
  • embedding-size: Embedding dimension. Default is 128
  • vocab-size: Vocabulary size. Default is 10000
  • max-length: The maximum length of a sentence you want to keep when preprocessing. Default value 256
  • learning-rate: Learning rate. Default is 0.005
  • optimizer: Choose one optimizer that you want to apply, including: rmsprop and adam. Default is rmsprop
  • test-size: Split to train (1-x) and test (x) dataset with ratio. Default is 0.2
  • batch-size: The batch size of the dataset. Default value 64

IV. Prediction Process

Ensure you already have trained for the tmp/model/${model} was created after successful training process. Use the script below to predict:

!python predict.py --review-sentence ${The review sentence} 
                --model-path ${model directory}
                --data-path ${data_path}
                --data-name ${data_name}
                --data-classes ${data_classes}       
                --vocab-size ${vocab_size}
                --max-length ${max_length}

Using GRU model for predictions:

!python predict.py --model-path tmp/model/gru.h5py --review-sentence "The plot of film is really good"

Other predictions with LSTM:

!python predict.py --model-path tmp/model/lstm.h5py --review-sentence "The plot of film is really good"

V. Result and Comparision

The arguments that we used for train:

  • epoch: 10
  • loss: rmsprop
  • metrics: ['accuracy', 'mse']

1. GRU

...
Epoch 6/10
1250/1250 [==============================] - 325s 260ms/step - loss: 0.1785 - accuracy: 0.9317 - mse: 0.0511 - val_loss: 0.3023 - val_accuracy: 0.8936 - val_mse: 0.0836
Epoch 7/10
1250/1250 [==============================] - 326s 261ms/step - loss: 0.1621 - accuracy: 0.9388 - mse: 0.0459 - val_loss: 0.3329 - val_accuracy: 0.8818 - val_mse: 0.0924
Epoch 8/10
1250/1250 [==============================] - 326s 261ms/step - loss: 0.1460 - accuracy: 0.9434 - mse: 0.0414 - val_loss: 0.3247 - val_accuracy: 0.8863 - val_mse: 0.0864
Epoch 9/10
1250/1250 [==============================] - 323s 258ms/step - loss: 0.1310 - accuracy: 0.9507 - mse: 0.0368 - val_loss: 0.3278 - val_accuracy: 0.8841 - val_mse: 0.0878
Epoch 10/10
1250/1250 [==============================] - 322s 258ms/step - loss: 0.1149 - accuracy: 0.9572 - mse: 0.0318 - val_loss: 0.3633 - val_accuracy: 0.8818 - val_mse: 0.0919

2. LSTM

...
Epoch 6/10
1250/1250 [==============================] - 352s 281ms/step - loss: 0.4320 - accuracy: 0.8328 - mse: 0.1327 - val_loss: 0.4471 - val_accuracy: 0.8203 - val_mse: 0.1405
Epoch 7/10
1250/1250 [==============================] - 350s 280ms/step - loss: 0.4057 - accuracy: 0.8458 - mse: 0.1232 - val_loss: 0.4552 - val_accuracy: 0.8326 - val_mse: 0.1386
Epoch 8/10
1250/1250 [==============================] - 350s 280ms/step - loss: 0.3847 - accuracy: 0.8532 - mse: 0.1166 - val_loss: 0.4161 - val_accuracy: 0.8353 - val_mse: 0.1303
Epoch 9/10
1250/1250 [==============================] - 349s 280ms/step - loss: 0.3255 - accuracy: 0.8714 - mse: 0.0977 - val_loss: 0.3246 - val_accuracy: 0.8581 - val_mse: 0.0993
Epoch 10/10
1250/1250 [==============================] - 350s 280ms/step - loss: 0.2795 - accuracy: 0.8878 - mse: 0.0830 - val_loss: 0.3019 - val_accuracy: 0.8795 - val_mse: 0.0905

3. TANH

...
Epoch 6/10
1250/1250 [==============================] - 103s 83ms/step - loss: 0.6915 - accuracy: 0.5132 - mse: 0.2491 - val_loss: 0.6915 - val_accuracy: 0.5115 - val_mse: 0.2492
Epoch 7/10
1250/1250 [==============================] - 103s 82ms/step - loss: 0.6851 - accuracy: 0.5284 - mse: 0.2460 - val_loss: 0.6787 - val_accuracy: 0.5420 - val_mse: 0.2428
Epoch 8/10
1250/1250 [==============================] - 103s 82ms/step - loss: 0.6713 - accuracy: 0.5501 - mse: 0.2395 - val_loss: 0.6785 - val_accuracy: 0.5443 - val_mse: 0.2427
Epoch 9/10
1250/1250 [==============================] - 103s 82ms/step - loss: 0.6715 - accuracy: 0.5508 - mse: 0.2396 - val_loss: 0.6908 - val_accuracy: 0.5250 - val_mse: 0.2485
Epoch 10/10
1250/1250 [==============================] - 103s 82ms/step - loss: 0.6731 - accuracy: 0.5497 - mse: 0.2403 - val_loss: 0.6936 - val_accuracy: 0.5233 - val_mse: 0.2494

4. Results

  • Base on the results of Training with 3 kinds of model, we explored that there are differences about the accuracy:
    • Both GRU and LSTM have high efficiency with the highest val_accuracy is 0.8936 and 0.8795, respectively.
    • The tanh model is the opposite, which its val_accuracy is really low with just around 0.54
  • The results clearly demonstrated the superiority of the gated-RNN units; both the LSTM unit and GRU, over the traditional tanh method.
  • However, we cannot make a concrete conclusion on which of the two gating units was better.