https://github.com/dizys/movie-review-sentiment-classification

Building a sentiment classifier that takes movie review text and output rating from 1 to 10 using Word2Vec, bidirectional LSTM, AWD LSTM and more.
https://github.com/dizys/movie-review-sentiment-classification

fastai jupyter-notebook pytorch sentiment-classification

Last synced: 15 days ago
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Building a sentiment classifier that takes movie review text and output rating from 1 to 10 using Word2Vec, bidirectional LSTM, AWD LSTM and more.

• Host: GitHub
• URL: https://github.com/dizys/movie-review-sentiment-classification
• Owner: dizys
• License: mit
• Created: 2019-04-27T22:05:14.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-04-28T06:36:19.000Z (over 5 years ago)
• Last Synced: 2024-12-20T20:03:07.426Z (21 days ago)
• Topics: fastai, jupyter-notebook, pytorch, sentiment-classification
• Language: Scheme
• Size: 42 MB
• Stars: 1
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Movie Review Sentiment Classification

> The project is done using Jupyter Notebook with Python 3.7, PyTorch 1.0.1, fastai 1.0.52, gensim, ...

Building a sentiment classifier that takes movie review text and output rating from 1 to 10 using `Word2Vec`, `Bidirectional LSTM`, `AWD LSTM` and more.

## Directory Structure

```

project

├─data

│  ├─test_set.ss                    Test dataset

│  ├─training_set.ss                Training dataset

│  └─validation.ss                  Validation dataset

├─images                            Notebook images

├─language

│  └─movie_corpus.txt               Corpus for training Word2Cec model  

├─rating_model_fastai.ipynb         Plan B notebook

├─rating_model.ipynb                Plan A notebook

├─word2vec.ipynb                    Word2Vec model training notebook

│

...

```

## Report

Reports with implementation introduction, code explanation and result analysis are all embedded in the notebooks for better coherence.

## Plan A: Bidirectional LSTM with word2vec as embedding

**Training Word2Vec model**

The corpus I used is a self-made 10M `movie review + Harry Potter` sentence collection. File at [language/movie_corpus.txt](./language/movie_corpus.txt). The dimension of Word2Vec model is 100.

Please see [word2vec.ipynb](./word2vec.ipynb)

**Training Classifier**

Mainly use PyTorch 

Please see [rating_model.ipynb](./rating_model.ipynb)

## Plan B: Transfer Learning LSTM using FastAI

Mainly use FastAI - an high-level library for easier working with PyTorch.

Please see [rating_model_fastai.ipynb](./rating_model_fastai.ipynb)

## Predicts of Test Set

Choosing the result of 'Plan B' for its better performance.

Please see [senti_output.ss](./senti_output.ss)

## License

MIT, see the [LICENSE](/LICENSE) file for details.