https://github.com/loristns/imdb_sentiment_analysis
https://github.com/loristns/imdb_sentiment_analysis
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/loristns/imdb_sentiment_analysis
- Owner: loristns
- Created: 2022-12-12T14:09:34.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2022-12-12T14:10:00.000Z (over 2 years ago)
- Last Synced: 2025-03-30T17:02:53.645Z (2 months ago)
- Language: HTML
- Size: 2.93 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# IMDB Sentiment Analysis
This is a simple Flask application that uses a pre-trained BERT or DistilBERT model to classify the sentiment of movie reviews.
We use fine-tuned BERT and DistilBERT models from Hugging Face Hub. The models are available at the following links:
- [lvwerra/bert-imdb](https://huggingface.co/lvwerra/bert-imdb)
- [lvwerra/distilbert-imdb](https://huggingface.co/lvwerra/distilbert-imdb)
Those models are licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
### Context
This project was created as part of a course about the Scientific Research environment at the University of La Rochelle.
It was a collaborative project between [Dorian Maidon](https://github.com/statickzz), [Aliou Ly](https://github.com/aliou-ly) and myself.
## Prerequisites
- Python 3
- PyTorch
- Transformers ([https://github.com/huggingface/transformers](https://github.com/huggingface/transformers))
- Flask
## Usage
To run the Flask application, use the following command:
```bash
python app.py
```
The application will then be available at http://localhost:5000/. To classify the sentiment of a movie review through the user interface, go to the homepage and enter a movie review in the input field.
To classify the sentiment of a movie review through the API endpoint, send a GET request to `http://localhost:5000/predict?text=&model=`.
The response will be a JSON object with the predicted sentiment.
## Dataset
The program uses either the BERT or DistilBERT model, which have been fine-tuned on IMDb movie reviews dataset.
```
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
```
## Further Reading
For more information on the BERT and DistilBERT models, please see the following papers:
- BERT: https://arxiv.org/abs/1810.04805
- DistilBERT: https://arxiv.org/abs/1910.01108