Using Diagnostic classifiers to evaluate the information encoded in the LCR-Rot-hop++ model which is used in Aspect based sentiment classification

All software is written in PYTHON3 (https://www.python.org/) and makes use of the TensorFlow framework (https://www.tensorflow.org/).

1. Download ontology: https://github.com/KSchouten/Heracles/tree/master/src/main/resources/externalData
2. Download SemEval2016 Dataset: http://alt.qcri.org/semeval2016/task5/index.php?id=data-and-tools
3. Download Stanford CoreNLP:https://stanfordnlp.github.io/CoreNLP/download.html
4. Download the files for the LCR-Rot-hop++ model: https://github.com/mtrusca/HAABSA\_PLUS\_PLUS
5. Make a "results" folder to save model.
6. Overwrite any files from the LCR-Rot-hop++ model with the files in this repository.
7. The pickle files containing the layer information used in the paper can be found on https://drive.google.com/drive/folders/1tBcBiOphU4DTlthwDZHkR86mv5wSXhYO?usp=sharing

• main_2.py: program to run . Each method can be activated by setting its corresponding boolean to True e.g. to run the lcr_Rot method set lcr_Rot = True.
• config.py: contains parameter configurations that can be changed such as: dataset_year, batch_size, iterations.
• utils.py: contains methods used in other files.

• lcr_v4.py: implementation for the LCR-Rot-hop++ algorithm version 4, a subclass of neural language model
• neural_language_model.py: implementation of neural language model algorithm, main class

• ontology_tagging.py: code creating hypothesis related to the ontology.

• attention_layers.py: implementation of the attention function
• nn_layers.py: implementation of the Bi-LSTM, and softmax layer

This code uses ideas and code of the following related papers:

• Zheng, S. and Xia, R. (2018). Left-center-right separated neural network for aspect-based sentiment analysis with rotatory attention. arXiv preprint arXiv:1802.00892.
• Schouten, K. and Frasincar, F. (2018). Ontology-driven sentiment analysis of product and service aspects. In Proceedings of the 15th Extended Semantic Web Conference (ESWC 2018), pages 608–623.
• Truşcǎ M.M., Wassenberg D., Frasincar F., Dekker R. (2020) A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical Attention. In: Bielikova M., Mikkonen T., Pautasso C. (eds) Web Engineering. ICWE 2020. Lecture Notes in Computer Science, vol 12128. Springer, Cham. https://doi-org.eur.idm.oclc.org/10.1007/978-3-030-50578-3_25
• Hupkes, D., Veldhoen, S., and Zuidema, W. H. (2018). Visualisation and ’diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure. J. Artif. Intell. Res., 61:907–926.