Unsupervised Aspect Extraction

Codes and Dataset for ACL2017 paper ‘‘An unsupervised neural attention model for aspect extraction’’. (pdf)

THIS FORK: Adoption to TensorFlow backend, Python 3, recent dependency versions

Data

You can find the pre-processed datasets and the pre-trained word embeddings in [Download]. The zip file should be decompressed and put in the main folder.

You can also download the original datasets of Restaurant domain and Beer domain in [Download]. For preprocessing, put the decompressed zip file in the main folder and run

python word2vec.py
python preprocess.py

respectively in code/ . The preprocessed files and trained word embeddings for each domain will be saved in a folder preprocessed_data/.

Train

Under code/ and type the following command for training:

python3 train.py \
--emb ../preprocessed_data/$domain/w2v_embedding \
--domain $domain \
-o output_dir \

where $domain in ['restaurant', 'beer'] is the corresponding domain, --emb is the path to the pre-trained word embeddings, -o is the path of the output directory. You can find more arguments/hyper-parameters defined in train.py with default values used in our experiments.

After training, two output files will be saved in code/output_dir/$domain/: 1) aspect.log contains extracted aspects with top 100 words for each of them. 2) model_param contains the saved model weights

Evaluation

Under code/ and type the following command:

python3 evaluation.py \
--domain $domain \
-o output_dir \

Note that you should keep the values of arguments for evaluation the same as those for training (except --emb, you don't need to specify it), as we need to first rebuild the network architecture and then load the saved model weights.

This will output a file att_weights that contains the attention weights on all test sentences in code/output_dir/$domain.

To assign each test sentence a gold aspect label, you need to first manually map each inferred aspect to a gold aspect label according to its top words, and then uncomment the bottom part in evaluation.py (line 136-144) for evaluaton using F scores.

One example of trained model for the restaurant domain has been put in pre_trained_model/restaurant/, and the corresponding aspect mapping has been provided in evaluation.py (line 136-139). You can uncomment line 28 in evaluation.py and run the above command to evaluate the trained model.

Dependencies

Python 3.6

• keras 2.2
• tensorflow 1.9
• numpy 1.15
• gensim 3.5

Other versions, e.g. slightly older/newer Python 3, may well work, just try it.

You can install all dependencies with the following command:

pip install -r requirements.txt

If you want to pre-process the data yourself, you'll also need NLTK and some of its data, which you can install like this from the Python 3 CLI:

import nltk
nltk.download('stopwords')
nltk.download('wordnet')

Cite

If you use the code, please cite the following paper:

@InProceedings{he-EtAl:2017:Long2,
  author    = {He, Ruidan  and  Lee, Wee Sun  and  Ng, Hwee Tou  and  Dahlmeier, Daniel},
  title     = {An Unsupervised Neural Attention Model for Aspect Extraction},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics}
}