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Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree

Summary

The paper addresses aspect-based sentiment classification: deciding whether a sentence conveys a positive, negative or neutral opinion toward one aspect term. The authors argue that dependency trees bring aspect terms and their opinion words close together, even in long sentences, and that this structure suits graph neural networks. Their model, CDT (convolution over dependency tree), works in three steps. A Bi-LSTM first produces contextual word vectors. A GCN then refines these vectors over the sentence's dependency graph, using self-loops and degree normalisation. Finally, only the aspect-token vectors are pooled into the representation passed to the softmax classifier. Stacking GCN layers lets information from opinion words reach the aspect nodes. This design follows earlier work that placed a GCN on top of an LSTM for relation extraction. The authors report that CDT is a new state of the art, although the excerpt omits the datasets and scores. They support the aspect-only pooling choice with masking experiments and attention visualisations. The study shows that a fairly simple model with syntax information can outperform more complex ones.

Key points

  • Architecture: Bi-LSTM encoder, then k GCN layers over the dependency adjacency matrix, then pooling over aspect tokens only, then softmax.
    • Each GCN layer uses ReLU and normalisation c_i = 1/degree.
    • k layers reach the k-th order neighbourhood of each word.
  • Pooling inconsistency: Figure 2 labels the aggregation step "Average Pool", but the analysis section describes max pooling.
  • Masking test: Inputs are zeroed one word at a time with the ASP-GCN variant to score each word's relevance. Opinion words score higher as more GCN layers are added.
  • Case studies: The aspects 'Sangria', 'paratha' and 'LASAGNA' are used to compare ASP-BiLSTM, TNet and CDT. Only CDT finds the right opinion words, such as 'FRESH' and 'BEST'.
  • Missing results: The provided text omits the benchmark names and accuracy/F1 figures, so check the original paper before citing numbers.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Natural language processing

Topics: Dependency parsing

Cited by

67 papers of our library cite it (of the 140884 whose bibliographies we read); 1 of them have a page here, 66 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/d19-1569

@inproceedings{sun2019aspect,
  title = {{Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree}},
  author = {Sun, Kai and Zhang, Richong and Mensah, Samuel and Mao, Yongyi and Liu, Xudong},
  booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
  year = {2019},
  doi = {10.18653/v1/d19-1569},
  url = {https://doi.org/10.18653/v1/d19-1569}
}

References

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