基于方面的情感分析，PyTorch实现-Python开发

# ABSA-PyTorch > Aspect Based Sentiment Analysis, PyTorch Implementations. > > 基于方面的情感分析，使用PyTorch实现。  [](https://gitter.im/ABSA-PyTorch/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) <!-- ALL-CONTRIBUTORS-BADGE:START - Do not remove or modify this section --> [](#contributors-) <!-- ALL-CONTRIBUTORS-BADGE:END --> ## Requirement * pytorch >= 0.4.0 * numpy >= 1.13.3 * sklearn * python 3.6 / 3.7 * transformers To install requirements, run `pip install -r requirements.txt`. * For non-BERT-based models, [GloVe pre-trained word vectors](https://github.com/stanfordnlp/GloVe#download-pre-trained-word-vectors) are required, please refer to [data_utils.py](./data_utils.py) for more detail. ## Usage ### Training ```sh python train.py --model_name bert_spc --dataset restaurant ``` * All implemented models are listed in [models directory](./models/). * See [train.py](./train.py) for more training arguments. * Refer to [train_k_fold_cross_val.py](./train_k_fold_cross_val.py) for k-fold cross validation support. ### Inference * Refer to [infer_example.py](./infer_example.py) for both non-BERT-based models and BERT-based models. ### Tips * For non-BERT-based models, training procedure is not very stable. * BERT-based models are more sensitive to hyperparameters (especially learning rate) on small data sets, see [this issue](https://github.com/songyouwei/ABSA-PyTorch/issues/27). * Fine-tuning on the specific task is necessary for releasing the true power of BERT. ## Reviews / Surveys Qiu, Xipeng, et al. "Pre-trained Models for Natural Language Processing: A Survey." arXiv preprint arXiv:2003.08271 (2020). [[pdf]](https://arxiv.org/pdf/2003.08271) Zhang, Lei, Shuai Wang, and Bing Liu. "Deep Learning for Sentiment Analysis: A Survey." arXiv preprint arXiv:1801.07883 (2018). [[pdf]](https://arxiv.org/pdf/1801.07883) Young, Tom, et al. "Recent trends in deep learning based natural language processing." arXiv preprint arXiv:1708.02709 (2017). [[pdf]](https://arxiv.org/pdf/1708.02709) ## BERT-based models ### BERT-ADA ([official](https://github.com/deepopinion/domain-adapted-atsc)) Rietzler, Alexander, et al. "Adapt or get left behind: Domain adaptation through bert language model finetuning for aspect-target sentiment classification." arXiv preprint arXiv:1908.11860 (2019). [[pdf](https://arxiv.org/pdf/1908.11860)] ### BERR-PT ([official](https://github.com/howardhsu/BERT-for-RRC-ABSA)) Xu, Hu, et al. "Bert post-training for review reading comprehension and aspect-based sentiment analysis." arXiv preprint arXiv:1904.02232 (2019). [[pdf](https://arxiv.org/pdf/1904.02232)] ### ABSA-BERT-pair ([official](https://github.com/HSLCY/ABSA-BERT-pair)) Sun, Chi, Luyao Huang, and Xipeng Qiu. "Utilizing bert for aspect-based sentiment analysis via constructing auxiliary sentence." arXiv preprint arXiv:1903.09588 (2019). [[pdf](https://arxiv.org/pdf/1903.09588.pdf)] ### LCF-BERT ([lcf_bert.py](./models/lcf_bert.py)) ([official](https://github.com/yangheng95/LCF-ABSA)) Zeng Biqing, Yang Heng, et al. "LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification." Applied Sciences. 2019, 9, 3389. [[pdf]](https://www.mdpi.com/2076-3417/9/16/3389/pdf) ### AEN-BERT ([aen.py](./models/aen.py)) Song, Youwei, et al. "Attentional Encoder Network for Targeted Sentiment Classification." arXiv preprint arXiv:1902.09314 (2019). [[pdf]](https://arxiv.org/pdf/1902.09314.pdf) ### BERT for Sentence Pair Classification ([bert_spc.py](./models/bert_spc.py)) Devlin, Jacob, et al. "Bert: Pre-training of deep bidirectional transformers for language understanding." arXiv preprint arXiv:1810.04805 (2018). [[pdf]](https://arxiv.org/pdf/1810.04805.pdf) ## Non-BERT-based models ### ASGCN ([asgcn.py](./models/asgcn.py)) ([official](https://github.com/GeneZC/ASGCN)) Zhang, Chen, et al. "Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks." Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. 2019. [[pdf]](https://www.aclweb.org/anthology/D19-1464) ### MGAN ([mgan.py](./models/mgan.py)) Fan, Feifan, et al. "Multi-grained Attention Network for Aspect-Level Sentiment Classification." Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018. [[pdf]](http://aclweb.org/anthology/D18-1380) ### AOA ([aoa.py](./models/aoa.py)) Huang, Binxuan, et al. "Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks." arXiv preprint arXiv:1804.06536 (2018). [[pdf]](https://arxiv.org/pdf/1804.06536.pdf) ### TNet ([tnet_lf.py](./models/tnet_lf.py)) ([official](https://github.com/lixin4ever/TNet)) Li, Xin, et al. "Transformation Networks for Target-Oriented Sentiment Classification." arXiv preprint arXiv:1805.01086 (2018). [[pdf]](https://arxiv.org/pdf/1805.01086) ### Cabasc ([cabasc.py](./models/cabasc.py)) Liu, Qiao, et al. "Content Attention Model for Aspect Based Sentiment Analysis." Proceedings of the 2018 World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2018. ### RAM ([ram.py](./models/ram.py)) Chen, Peng, et al. "Recurrent Attention Network on Memory for Aspect Sentiment Analysis." Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017. [[pdf]](http://www.aclweb.org/anthology/D17-1047) ### MemNet ([memnet.py](./models/memnet.py)) ([official](https://drive.google.com/open?id=1Hc886aivHmIzwlawapzbpRdTfPoTyi1U)) Tang, Duyu, B. Qin, and T. Liu. "Aspect Level Sentiment Classification with Deep Memory Network." Conference on Empirical Methods in Natural Language Processing 2016:214-224. [[pdf]](https://arxiv.org/pdf/1605.08900) ### IAN ([ian.py](./models/ian.py)) Ma, Dehong, et al. "Interactive Attention Networks for Aspect-Level Sentiment Classification." arXiv preprint arXiv:1709.00893 (2017). [[pdf]](https://arxiv.org/pdf/1709.00893) ### ATAE-LSTM ([atae_lstm.py](./models/atae_lstm.py)) 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. ### TD-LSTM ([td_lstm.py](./models/td_lstm.py), [tc_lstm.py](./models/tc_lstm.py)) ([official](https://drive.google.com/open?id=17RF8MZs456ov9MDiUYZp0SCGL6LvBQl6)) 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) ### LSTM ([lstm.py](./models/lstm.py)) Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9.8 (1997): 1735-1780. [[pdf](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.676.4320&rep=rep1&type=pdf)] ## Note on running with RTX30* If you are running on RTX30 series there may be some compatibility issues between installed/required versions of torch, cuda. In that case try using `requirements_rtx30.txt` instead of `requirements.txt`. ## Contributors Thanks goes to these wonderful people: <!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section --> <!-- prettier-ignore-start --> <!-- markdownlint-disable --> <table> <tr> <td align="center"><a href="https://github.com/AlbertoPaz"><img src="https://avatars2.githubusercontent.com/u/36967362?v=4?s=100" width="100px;" alt=""/><br />_{<b>Alberto Paz</b>}</a><br /><a href="https://github.com/songyouwei/ABSA-PyTorch/commits?author=AlbertoPaz" title="Code">💻</a></td> <td align="center"><a href="http://taojiang0923@gmail.com"><img src="https://avatars0.githubuserconten