Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks (1909.03477v2)

Abstract: Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification. However, these models lack a mechanism to account for relevant syntactical constraints and long-range word dependencies, and hence may mistakenly recognize syntactically irrelevant contextual words as clues for judging aspect sentiment. To tackle this problem, we propose to build a Graph Convolutional Network (GCN) over the dependency tree of a sentence to exploit syntactical information and word dependencies. Based on it, a novel aspect-specific sentiment classification framework is raised. Experiments on three benchmarking collections illustrate that our proposed model has comparable effectiveness to a range of state-of-the-art models, and further demonstrate that both syntactical information and long-range word dependencies are properly captured by the graph convolution structure.

Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks

The paper "Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks" introduces a novel approach to tackling the shortcomings of traditional sentiment classification methods by leveraging Graph Convolutional Networks (GCNs). This research emphasizes integrating syntactical dependencies and long-range word relations in aspect-based sentiment classification tasks, which have been less effectively addressed by prior models predominantly relying on attention mechanisms and Convolutional Neural Networks (CNNs).

In the field of aspect-based sentiment classification, models aim to detect sentiment polarities of specific aspects within a sentence. Despite advancements made through the adoption of neural networks, conventional approaches such as attention coupled with Recurrent Neural Networks (RNNs) have been insufficient in modeling syntactical dependencies and handling long-range dependencies effectively. The paper proposes the Aspect-specific Graph Convolutional Network (ASGCN) to address these limitations.

Contribution and Methodology

The primary contribution of the paper is the introduction of Aspect-specific Graph Convolutional Networks (ASGCNs) for aspect-based sentiment classification. The proposed model is among the first to deploy GCNs to exploit syntactical structures and dependencies in sentiment classification tasks. Key components of their approach include:

1. Graph Convolutional Network (GCN): In contrast to the traditional attention-based models, ASGCN employs a multi-layer GCN structure over dependency trees to effectively capture syntactical relationships between aspect terms and context words in a sentence.
2. Aspect-specific Masking: To enhance feature extraction, ASGCN integrates a masking layer that filters out non-aspect words, thereby emphasizing aspect-specific features. This mechanism is crucial for aspect-oriented feature extraction, distinguishing it from general sentiment classification methodologies.
3. Attention Mechanism with Contextual Awareness: The model incorporates an aspect-aware attention mechanism that refines the representation of context using retrieved aspect-oriented features, ensuring robust capture of semantic relatedness.

The experiments conducted on various benchmark datasets demonstrate the comparative performance of ASGCN models against several state-of-the-art methods. The models achieved notable results, particularly on datasets sensitive to syntactic structures, indicating the efficacy of GCNs in enriching semantic representations with syntactical constraints.

Implications and Future Directions

This research illuminates a path for future sentiment analysis models by effectively utilizing syntactic information and word dependencies, which are often underexplored in traditional neural architectures. The implications of this research extend to various natural language processing applications requiring nuanced comprehension of context-specific sentiment.

Moving forward, enhancing ASGCN could involve incorporating edge labels from dependency trees to further refine the syntactical information embedded within the model. Additionally, exploring domain-specific embeddings and syntactic tree variations could further improve contextual understanding. There is also potential in expanding ASGCN's capabilities to handle multiple aspects within a sentence simultaneously, fostering models that can better interpret complex expressions involving multiple sentiment-bearing entities.

This paper provides a significant advancement in aspect-based sentiment classification, offering a robust framework that adeptly integrates syntactical insights into the neural architecture, which may serve as an inspiration for more sophisticated approaches in the field.