https://github.com/hiyouga/pban-pytorch
A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis, PyTorch implementation.
https://github.com/hiyouga/pban-pytorch
aspect-based-sentiment-analysis attention-model deep-learning natural-language-processing nlp pytorch sentiment-analysis
Last synced: 27 days ago
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A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis, PyTorch implementation.
- Host: GitHub
- URL: https://github.com/hiyouga/pban-pytorch
- Owner: hiyouga
- License: mit
- Created: 2019-03-23T10:12:28.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-11-23T08:57:41.000Z (almost 6 years ago)
- Last Synced: 2024-12-29T13:32:19.449Z (9 months ago)
- Topics: aspect-based-sentiment-analysis, attention-model, deep-learning, natural-language-processing, nlp, pytorch, sentiment-analysis
- Language: Python
- Size: 1.29 MB
- Stars: 36
- Watchers: 4
- Forks: 23
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PBAN-PyTorch
> [PyTorch](https://pytorch.org/) implementation of [Gu et al.'s COLING 2018](https://aclweb.org/anthology/C18-1066) work.
[](LICENSE)
## Requirement
* PyTorch >= 0.4.0
* NumPy >= 1.13.3
* Python 3.6
* GloVe pre-trained word vectors:
* Download pre-trained word vectors [here](https://github.com/stanfordnlp/GloVe#download-pre-trained-word-vectors).
* Extract the [glove.twitter.27B.zip](http://nlp.stanford.edu/data/wordvecs/glove.twitter.27B.zip) and [glove.42B.300d.zip](http://nlp.stanford.edu/data/wordvecs/glove.42B.300d.zip) to the `\glove\` folder.
## Dataset
Based on the restaurant and laptop dataset of [SemEval-2014 Task 4](http://alt.qcri.org/semeval2014/task4/).
### Restaurant Dataset
| Polarity | #Positive | #Negative | #Neutral |
| -------- | --------- | --------- | -------- |
| Train | 2164 | 807 | 637 |
| Test | 728 | 196 | 196 |
### Laptop Dataset
| Polarity | #Positive | #Negative | #Neutral |
| -------- | --------- | --------- | -------- |
| Train | 994 | 870 | 464 |
| Test | 341 | 128 | 169 |
## Usage
Train the model:
```sh
python train.py --model_name pban --dataset restaurant
```
Show help message and exit:
```sh
python train.py -h
```
## Implemented models
### LSTM
Tang, Duyu, et al. "Effective LSTMs for Target-Dependent Sentiment Classification." Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. 2016. [[pdf]](https://arxiv.org/pdf/1512.01100)
### ATAE-LSTM
Wang, Yequan, Minlie Huang, and Li Zhao. "Attention-based LSTM for aspect-level sentiment classification." Proceedings of the 2016 conference on empirical methods in natural language processing. 2016. [[pdf]](http://www.aclweb.org/anthology/D16-1058)
### PBAN
Gu, Shuqin, et al. "A Position-aware Bidirectional Attention Network for Aspect-level Sentiment Analysis." Proceedings of the 27th International Conference on Computational Linguistics. 2018. [[pdf]](http://www.aclweb.org/anthology/C18-1066)
## Performance
### Restaurant Dataset
#### Three-class
| Model | In Paper | This Code |
| --------- | --------- | --------- |
| LSTM | 74.28 | 77.68 |
| ATAE-LSTM | 77.20 | 78.30 |
| PBAN | **81.16** | **80.89** |
#### Two-class
| Model | In Paper | This Code |
| --------- | --------- | --------- |
| LSTM | - | - |
| ATAE-LSTM | 90.90 | 90.26 |
| PBAN | **91.67** | **92.32** |
### Laptop Dataset
#### Three-class
| Model | In Paper | This Code |
| --------- | --------- | --------- |
| LSTM | 66.45 | 71.00 |
| ATAE-LSTM | 68.70 | 71.32 |
| PBAN | **74.12** | **74.76** |
#### Two-class
| Model | In Paper | This Code |
| --------- | --------- | --------- |
| LSTM | - | - |
| ATAE-LSTM | 87.60 | **87.63** |
| PBAN | **87.81** | 87.42 |
## Acknowledgements
* Some of the code is borrowed from [songyouwei](https://github.com/songyouwei/ABSA-PyTorch).
* Using this code means you have read and accepted the copyrights set by the dataset providers.
## License
MIT