Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks

By Bin Liang, Hang Su, Lin Gui, Erik Cambria, Ruifeng Xua. Knowledge-Based Systems, 2021

456 citations recorded by the source corpus.

Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which needs to detection the sentiment polarity towards a given aspect. Recently, graph neural models over the dependency tree are widely applied for aspect-based sentiment analysis. Most existing works, however, they generally focus on learning the dependency information from contextual words to aspect words based on the dependency tree of the sentence, which lacks the exploitation of contextual affective knowledge with regard to the specific aspect. In this paper, we propose a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN . To be specific, we explore a novel solution to construct the graph neural networks via integrating the affective knowledge from SenticNet to enhance the dependency graphs of sentences. Based on it, both the dependencies of contextual words and aspect words and the affective information between opinion words and the aspect are considered by the novel affective enhanced graph model. Experimental results on multiple public benchmark datasets illustrate that our proposed model can beat state-of-the-art methods.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/9d1dedbae6ea794907127b12eacc9e0cfabac098

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shuyong Gao
2. Weizhen Song
3. Ciyuan Peng
4. Antonio Liotta
5. Xinlei Li
6. Francesco Osborne
7. Da-Wei Yang
8. Wei Tao

Citations in the corpus

• Knowledge Graphs: Opportunities and Challenges (2023, background): “Nowadays, knowledge graphs are widely utilized by AI systems (Liang et al. 2022), such as recommenders, question-answering systems, and information retrieval tools.”
• A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, methodology): “To decrease the training time and complexity based on LSTM and attention mechanisms to predict the sentiment polarity, Xue et al. [151] designed a Gated Convolutional network with Aspect Embedding (GCAE) for ACSA and ATSA, which are more efficient and simpler than the RNN-based model.”