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| # Sentiment Analysis | |
| ## Overview | |
| This is an implementation of the Sentiment Analysis model as described in the [this paper](https://arxiv.org/abs/1412.1058). The implementation is with the reference to [paddle version](https://github.com/mlperf/reference/tree/master/sentiment_analysis/paddle). | |
| The model makes use of concatenation of two CNN layers with different kernel sizes. Batch normalization and dropout layers are used to prevent over-fitting. | |
| ## Dataset | |
| The [keras](https://keras.io)'s [IMDB Movie reviews sentiment classification](https://keras.io/datasets/#imdb-movie-reviews-sentiment-classification) dataset is used. The dataset file download is handled by keras module, and the downloaded files are stored at ``~/.keras/datasets` directory. The compressed file's filesize as of June 15 2018 is 17MB. | |
| ## Running Code | |
| ### Train and evaluate model | |
| To train and evaluate the model, issue the following command: | |
| ``` | |
| python sentiment_main.py | |
| ``` | |
| Arguments: | |
| * `--dataset`: The dataset name to be downloaded and preprocessed. By default, it is `imdb`. | |
| There are other arguments about models and training process. Use the `--help` or `-h` flag to get a full list of possible arguments with detailed descriptions. | |
| ## Benchmarks | |
| The model was recorded to have the accuracy of 90.1% for the IMDB dataset. | |