Aspect-based Sentiment Analysis with Graph Convolution over Syntactic Dependencies

PyTorch implementation of aspect-based sentiment analysis with graph convolution over dependency parse trees of health and well-being related content.

Repository

• data folder contains preprocessed training, validation and test dataset.
• glove_dictionary.py download GloVe model and execute this script in order to create a dictionary.
• model.py contains the implementation of the model.
• main.py is the script that contains training, validation and testing of the model.

References

• Gräßer F, Kallumadi S, Malberg H, Zaunseder S. Aspect-based sentiment analysis of drug reviews applying cross-domain and cross-data learning. In Proceedings of the 2018 International Conference on Digital Health 2018 Apr 23 (pp. 121-125).
• Pennington J, Socher R, Manning CD. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) 2014 Oct (pp. 1532-1543).
• Hamilton W.L, Ying R, Leskovec J. Inductive Representation Learning on Large Graphs. NIPS, 2017.
• Early Stopping for PyTorch
• Chen D, Manning CD. A fast and accurate dependency parser using neural networks. InProceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) 2014 Oct (pp. 740-750).