Aspect-level Sentiment Classification
Code and dataset for ACL2018 [paper] ‘‘Exploiting Document Knowledge for Aspect-level Sentiment Classification’’.
Data
The preprocessed aspect-level datasets can be downloaded at [Download], and the document-level datasets can be downloaded at [Download]. The zip files should be decompressed and put in the main folder.
The pre-trained Glove vectors (on 840B tokens) are used for initializing word embeddings. You can download the extracted subset of Glove vectors for each dataset at [Download], the size of which is much smaller. The zip file should be decompressed and put in the main folder.
Training and evaluation
Pretraining on document-level dataset
The pretrained weights from document-level examples used in our experiments are provided at pretrained_weights/. You can use them directly for initialising aspect-level models.
Or if you want to retrain on ducment-level again, execute the command below under code_pretrain/:
CUDA_VISIBLE_DEVICES="0" python pre_train.py \
--domain $domain \
where $domain in ['yelp_large', 'electronics_large'] denotes the corresponding document-level domain. The trained model parameters will be saved under pretrained_weights/. You can find more arguments defined in pre_train.py with default values used in our experiments.
Training and evaluation on aspect-level dataset
To train aspect-level sentiment classifier, excute the command below under code/:
CUDA_VISIBLE_DEVICES="0" python train.py \
--domain $domain \
--alpha 0.1 \
--is-pretrain 1 \
where $domain in ['res', 'lt', 'res_15', 'res_16'] denotes the corresponding aspect-level domain. --alpha denotes the weight of the document-level training objective (\lamda in the paper). --is-pretrain is set to either 0 or 1, denoting whether to use pretrained weights from document-level examples for initialisition. You can find more arguments defined in train.py with default values used in our experiments. At the end of each epoch, results on training, validation and test sets will be printed respectively.
Dependencies
- Python 2.7
- Keras 2.1.2
- tensorflow 1.4.1
- numpy 1.13.3
Cite
If you use the code, please cite the following paper:
@InProceedings{he-EtAl:2018,
author = {He, Ruidan and Lee, Wee Sun and Ng, Hwee Tou and Dahlmeier, Daniel},
title = {Exploiting Document Knowledge for Aspect-level Sentiment Classification},
booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics},
publisher = {Association for Computational Linguistics}
}
![ValueError: All input arrays (x) should have the same number of samples. Got array shapes: [(2, 74), (2, 18), (32, 572)]](https://avatars.githubusercontent.com/u/33858059?v=4)
95 Jan 8, 2023
80 Dec 8, 2022
184 Dec 11, 2022
34 Dec 28, 2022
174 Dec 22, 2022
109 Dec 29, 2022
68 Jul 18, 2022
62 Dec 27, 2022
34 Nov 15, 2022
75 Nov 2, 2022
3 Jan 3, 2023
27 Dec 20, 2022
47 Nov 2, 2022
35 Nov 25, 2022
22 Nov 9, 2022
256 Jan 5, 2023
11 Jul 28, 2022