ABSC, a project for aspect-based sentiment classification

Requirements

• python 3.5
• pytorch 0.4
• tensorboardX
• absl-py
• nltk
• tqdm

Usage

# prepro
python -m lstm.main --mode prepro
# train
python -m lstm.main --mode train
# test
python -m lstm.main --mode test
# You can set different parameters or use different models and datasets.

Experiment Result

Models	Restaurant_category	Restaurant	Laptop
lstm	-	-	-
atae_lstm	-	77.86/65.59	68.34/62.64
acsa_gcae	-	78.12/65.59	70.85/64.66
bilstm_att_g	-	76.34/63.65	69.91/63.20
acsa_gcae_g	-	-	-
ram	-	78.66/66.66	73.82/68.80
tnet	-	78.93/63.65	72.57/65.13

Direcotory

• data: the semeval2014 dataset
• lstm: lstm model
• 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.
• bilstm_att_g: Liu, Jiangming, and Yue Zhang. "Attention modeling for targeted sentiment." Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. Vol. 2. 2017.
• acsa_gcae: Xue, Wei, and Tao Li. "Aspect Based Sentiment Analysis with Gated Convolutional Networks." arXiv preprint arXiv:1805.07043 (2018).
• acsa_gcae: acsa_gcae + gates
• tnet: Li, Xin, et al. "Transformation Networks for Target-Oriented Sentiment Classification." arXiv preprint arXiv:1805.01086 (2018).

Note

Not every implementation works exactly the same as the original paper, if you find any problems in the implementation, please tell me([email protected]).