A model for implicit aspect sentiment analysis.
This is the source code for the paper: Murtadha, Ahmed, et al. "BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis" [1].
The datasets used in our experminents can be downloaded from this SemEval.
Required packages are listed in the requirements.txt file:
pip install -r requirements.txt
- Generate seed words for a given dataset (e.g., semeval):
- Go to L-LDA/
- Run the following code
python run_l_LDA.py --dataset semeval
The original code of L-LDA is publicly available
- Generate the semantic candidates:
- Go to ASC_generating/
- The processed data and embedings for restaurant is available. Note that these files were orginally proccessed by Ruidan He
- To process your own data and embeddings, put your data file in datasets then run this code:
python preprocessing.py python generate_domain_embedding.py- Run the following code to extract the semantic candidates
python semantic_candidate_generating.py --dataset semeval - Generate the synticatic candidates:
- Run the following code to generate the synticatic informatiom
python ASC_generating/opinion_words_extracting.py --dataset semeval
- To train BERT-ASC:
- Go to code/
- Run the following code
python code/run.py --dataset semeval --device cuda:0- The params could be :
- --dataset ={semeval,sentihood}
- --device ="cuda:0"
- To evaluate BERT-ASC:
- Go to code/
- Run the following code
python code/evaluate.py --dataset semeval --device cuda:0- The params could be :
- --dataset ={semeval,sentihood}
- --device ="cuda:0"
If you use the code, please cite the paper:
@article{murtadha2022bert,
title={BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis},
author={Murtadha, Ahmed and Pan, Shengfeng and Wen, Bo and Su, Jianlin and Zhang, Wenze and Liu, Yunfeng},
journal={arXiv preprint arXiv:2203.11702},
year={2022}
}