基于极性知识指导的短文本情感分析建模
Polarity-GuidedShort Text Sentiment Analysis Modeling
摘要: 情感分析研究领域中，深度神经网络技术成为目前的主流研究。相比于其他文本分类任务，情感分析任务的研究更加侧重于分析文本的主观极性特征。胶囊神经网络模型的提出为极性建模提供了一种实现的思路。我们在胶囊网络的基础上提出了一种全新的情感分析模型，并提出了一种基于极性知识指导的建模方法。实验证明，在同等条件下，新型情感分析模型可以取得更有效的极性学习能力。
Abstract: In the research of sentiment analysis, deep neural network technology has become the mainstream research. Compared with other text classification tasks, sentiment analysis task focuses more on an-alyzing the subjective polarity of the text. The capsule neural network model provides a direction for polarity modeling. We propose a novel sentiment analysis model based on capsule network and a knowledge-based modeling method. The results in experiments show that the proposed sentiment analysis model can get more effective polar learning ability under the same conditions.
文章引用：庞劲羽. 基于极性知识指导的短文本情感分析建模[J]. 仪器与设备, 2020, 8(4): 124-130. https://doi.org/10.12677/IaE.2020.84016

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