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https://github.com/muhammedbuyukkinaci/tensorflow-sentiment-analysis-on-amazon-reviews-data
Implementing different RNN models (LSTM,GRU) & Convolution models (Conv1D, Conv2D) on a subset of Amazon Reviews data with TensorFlow on Python 3. A sentiment analysis project.
https://github.com/muhammedbuyukkinaci/tensorflow-sentiment-analysis-on-amazon-reviews-data
amazon-reviews gated-recurrent-unit gated-recurrent-units gru long-short-term-memory-models low-level lstm lstm-neural-networks lstm-sentiment-analysis lstm-sentiment-classification sentiment-analysis sentiment-classification tensorflow tensorflow-api tensorflow-examples tensorflow-experiments tensorflow-gpu tensorflow-tutorials text-classification text-classifier
Last synced: 3 days ago
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Implementing different RNN models (LSTM,GRU) & Convolution models (Conv1D, Conv2D) on a subset of Amazon Reviews data with TensorFlow on Python 3. A sentiment analysis project.
- Host: GitHub
- URL: https://github.com/muhammedbuyukkinaci/tensorflow-sentiment-analysis-on-amazon-reviews-data
- Owner: MuhammedBuyukkinaci
- Created: 2019-01-06T09:29:03.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2019-09-21T12:02:20.000Z (about 5 years ago)
- Last Synced: 2024-11-12T17:03:58.567Z (3 days ago)
- Topics: amazon-reviews, gated-recurrent-unit, gated-recurrent-units, gru, long-short-term-memory-models, low-level, lstm, lstm-neural-networks, lstm-sentiment-analysis, lstm-sentiment-classification, sentiment-analysis, sentiment-classification, tensorflow, tensorflow-api, tensorflow-examples, tensorflow-experiments, tensorflow-gpu, tensorflow-tutorials, text-classification, text-classifier
- Language: Python
- Homepage:
- Size: 60.5 MB
- Stars: 52
- Watchers: 4
- Forks: 17
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# TensorFlow-Sentiment-Analysis-on-Amazon-Reviews-Data
Implementing different RNN([GRU](https://arxiv.org/pdf/1412.3555.pdf) & [LSTM](https://www.bioinf.jku.at/publications/older/2604.pdf)) models on a subset of Amazon Reviews data.
# Requirements
```
pip3 install -r requirements.txt
```
# Pre-trained Word Embeddings
I used 100-Dimensional [GloVe Word Embeddings](https://nlp.stanford.edu/projects/glove/) for this project.
You can download it from [here](https://www.kaggle.com/terenceliu4444/glove6b100dtxt#glove.6B.100d.txt).
After downloading, _glove.6B.100d.txt_ must be in where .py files are.
# Default Version
If you have GPU, use ``` 02_CUDNN_GRU.py ``` or ``` 06_CUDNN_LSTM.py ``` as default .
If you don't have, use ``` 04_CPU_Optimized_GRU.py ``` or ``` 08_CPU_Optimized_LSTM.py ``` as default.
# Training
``` python3 01_Baseline_GPU.py ```
``` python3 02_CUDNN_GRU.py ```
``` python3 03_CUDNN_GRU_bidirectional.py ```
``` python3 04_CPU_Optimized_GRU.py ```
``` python3 05_Baseline_LSTM.py ```
``` python3 06_CUDNN_LSTM.py ```
``` python3 07_CUDNN_LSTM_bidirectional.py ```
``` python3 08_CPU_Optimized_LSTM.py ```
``` python3 09_CONV1D_CUDNNGRU.py ```
``` python3 10_CONV2D.py ```
``` python3 11_Attention_GRU.py ```
``` python3 12_Attention_CUDNNGRU.py ```
``` python3 13_Attention_CUDNNGRU_bidirectional.py ```
# Dataset
Dataset used in this project is a subset of [Amazon Reviews](https://www.kaggle.com/bittlingmayer/amazonreviews#train.ft.txt.bz2).
Train dataset has 150k rows.
Test dataset has 30k rows.
Output classes are positive and negative (Binary Classification).
The models were trained on train dataset and validated on test dataset.
# Models
``` 01_Baseline_GRU.py ``` --> Base GRU implementation.
``` 02_CUDNN_GRU.py ``` --> GPU optimized CUDNNGRU implementation.
``` 03_CUDNN_GRU_bidirectional.py ``` --> GPU optimized CUDNNGRU bidirectional implementation.
``` 04_CPU_Optimized_GRU.py ``` --> CPU optimized GRU implementation.
``` 05_Baseline_LSTM.py ``` --> Base LSTM implementation.
``` 06_CUDNN_LSTM.py ``` --> GPU optimized CUDNNLSTM implementation.
``` 07_CUDNN_LSTM_bidirectional.py ``` --> GPU optimized CUDNNLSTM bidirectional implementation.
``` 08_CPU_Optimized_LSTM.py ``` --> CPU optimized LSTM implementation.
``` 09_CONV1D_CUDNNGRU.py ``` --> CONV1D BEFORE CUDNNGRU implementation
``` 10_CONV2D.py ``` ---> CONV2D implementation before fully connected layers.
``` 11_Attention_GRU.py ``` ---> Attention Layer including GRU implementation
``` 12_Attention_CUDNNGRU.py ``` ---> Attention Layer including CUDNNGRU implementation
``` 13_Attention_CUDNNGRU_bidirectional.py ``` ---> Attention Layer including Bidirectional CUDNNGRU implementation
# Early Stopping
I defined a customized function to check if loss is decreasing on test data.
If it isn't decreasing for a time period, the model stops to train.
``` Python
#Defining a function for early stopping
def early_stopping_check(x):
if np.mean(x[-20:]) <= np.mean(x[-80:]):
return True
else:
return False
```
# Results
