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Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification (1906.04501v1)

Published 11 Jun 2019 in cs.CL and cs.LG

Abstract: Aspect-level sentiment classification aims to distinguish the sentiment polarities over one or more aspect terms in a sentence. Existing approaches mostly model different aspects in one sentence independently, which ignore the sentiment dependencies between different aspects. However, we find such dependency information between different aspects can bring additional valuable information. In this paper, we propose a novel aspect-level sentiment classification model based on graph convolutional networks (GCN) which can effectively capture the sentiment dependencies between multi-aspects in one sentence. Our model firstly introduces bidirectional attention mechanism with position encoding to model aspect-specific representations between each aspect and its context words, then employs GCN over the attention mechanism to capture the sentiment dependencies between different aspects in one sentence. We evaluate the proposed approach on the SemEval 2014 datasets. Experiments show that our model outperforms the state-of-the-art methods. We also conduct experiments to evaluate the effectiveness of GCN module, which indicates that the dependencies between different aspects is highly helpful in aspect-level sentiment classification.

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Authors (3)
  1. Pinlong Zhaoa (1 paper)
  2. Linlin Houb (1 paper)
  3. Ou Wua (1 paper)
Citations (165)
View on Semantic Scholar

Summary

Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification

The paper "Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment Classification" addresses the task of aspect-level sentiment classification, focusing on the identification of sentiment polarities related to specific aspects within a sentence. This work significantly advances the field of NLP by proposing a novel approach that utilizes Graph Convolutional Networks (GCNs) to capture dependencies between multiple aspects within a sentence, which traditional methods tend to overlook.

Summary of Methodology

The authors introduce a model called SDGCN, which effectively uses GCNs to model sentiment dependencies, recognizing the interconnected sentiment influence between different aspects. The process starts by encoding input words using pre-trained embeddings from GloVe and BERT, which are then processed using Bi-LSTM to capture contextual information. A bidirectional attention mechanism is employed to accentuate aspect-specific representation, assisted by position encoding that prioritizes closer context words due to their impact on sentiment polarity. Following this, the GCN captures sentiment dependencies through node interactions in a constructed sentiment graph, effectively bridging gaps missed by previous methods.

Evaluation and Results

Evaluated on SemEval 2014 datasets, the SDGCN model exhibits superior performance compared to several state-of-the-art baseline models, including TD-LSTM, ATAE-LSTM, and RAM, among others. Notably, the model incorporating BERT embeddings achieves the highest accuracy and Macro-F1 scores, showcasing the profound impact of leveraging contextual embeddings and graph-based dependency modeling. Experiments confirm that both adjacent-relation and global-relation graph configurations contribute to detecting aspect interdependencies, although global-relation graphs slightly outperform due to their more comprehensive representation of interactions.

Implications and Future Work

The implications of this research are multifaceted, touching upon practical and theoretical domains in sentiment analysis. The practical capability of SDGCN to discern nuanced sentiment dependencies across aspects positions it as a valuable tool for enhanced sentiment analysis applications, such as consumer reviews and social media monitoring. Theoretically, this work expands the understanding of sentiment analysis by integrating graph-based approaches, providing a foundation for further exploration into graph structures and dependency modeling in NLP tasks.

Looking ahead, promising avenues include refining sentiment graph structures for more precise dependency modeling and exploring other graph neural network architectures to boost classification performance. Additionally, integrating contextual knowledge drawn from larger datasets or real-time interaction mapping could provide richer sentiment insight, fostering advancements in adaptive, context-aware sentiment analysis systems.

In conclusion, the paper presents a robust framework with impactful results, laying the groundwork for future studies in graph-based sentiment modeling within aspect-level classifications. The insightful coupling of GCNs with attention mechanisms serves as a compelling strategy to enhance the interpretability and accuracy of sentiment analysis tasks in complex linguistic environments.