Store, share and transfer: Learning and updating sentiment knowledge for aspect-based sentiment analysis
Store, share and transfer: Learning and updating sentiment knowledge for aspect-based sentiment analysis
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DOI：
10.1016/j.ins.2023.03.102
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Yongqiang Zheng;xia li;Jian-Yun Nie
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文献类型：
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作者：
Yongqiang Zheng;xia li;Jian-Yun Nie
以前的研究表明，将情感知识（例如，情感得分）对于基于方面的情感分析（ABSA）是有效的。然而，情感知识用于创建静态特征，这些特征不能在整个语料库中传播。与以往研究者不同的是，我们设计了一个具有存储、更新和共享功能的语料库级情感知识融合机制，可以帮助模型更好地理解数据集中各种观点词的情感信息。具体来说，我们首先为每个句子构建一个依赖图，并通过体词和上下文词之间的相对距离来细化边的权重。然后，我们在图中引入了两个特殊的情感知识节点，通过利用外部情感词典与意见词建立联系。我们将这两个节点设置为全局共享和可更新，这使得模型能够学习语料库级别和特定领域的情感知识。这些知识可以帮助模型生成更好的方面表示，其中包含丰富的上下文信息和情感知识。在多个公共数据集上进行了大量实验，实验结果表明了该方法的有效性。我们还分析了使用学习的语料库级别的情感知识在不同数据集之间传输的性能增益。
Previous studies have shown that incorporating sentiment knowledge (e.g., sentiment scores) is effective for aspect-based sentiment analysis (ABSA). However, sentiment knowledge is used to create static features, which cannot be propagated over an entire corpus. Unlike previous researchers, we designed a corpus-level sentiment knowledge fusion mechanism with storage, update, and sharing functions, which can help the model to better understand the sentiment information of various opinion words in the dataset. Specifically, we first constructed a dependency graph for each sentence and refined the weights of the edges by the relative distance between the aspect terms and context words. We then introduced two special sentiment knowledge nodes in the graph to establish connections with opinion words by leveraging external sentiment lexicons. We set these two nodes to be globally shared and updatable, which allowed the model to learn corpus-level and domain-specific sentiment knowledge. This knowledge can help the model to generate a better aspect representation that contains rich contextual information and sentiment knowledge. Extensive experiments were conducted on several public datasets, and the experimental results demonstrated the effectiveness of our method. We also analyzed the performance gains from using learned corpus-level sentiment knowledge to transfer across different datasets.