BERTCaps：面向波斯语多领域情感分析的BERT胶囊网络
BERTCaps: BERT Capsule for Persian Multi-Domain Sentiment Analysis
AI总结：
针对现有多领域情感分析方法跨领域适用性差的问题，提出结合BERT与胶囊模型的BERTCaps方法用于波斯语多领域情感分析，在Digikala数据集上取得了优异的分类效果。
摘要：
多领域情感分析旨在利用领域特定信息估计非结构化文本的极性。现有文献中方法的一个共同主要问题是，它们对与构建意见模型所用领域不同的其他领域适用性较差。本文提出一种基于深度学习方法的波斯语多领域情感分析新方法。所提出的BERTCapsules方法由BERT与胶囊（Capsule）模型结合而成：该方法中，BERT用于实例表示，胶囊结构用于学习提取到的图结构。我们采用包含10个领域、覆盖正负两种极性的Digikala数据集评估该方法。实验结果显示，BERTCaps模型在情感二分类任务上准确率达0.9712，在领域分类任务上准确率达0.8509。
英文摘要：
Multidomain sentiment analysis involves estimating the polarity of an unstructured text by exploiting domain specific information. One of the main issues common to the approaches discussed in the literature is their poor applicability to domains that differ from those used to construct opinion models.This paper aims to present a new method for Persian multidomain SA analysis using deep learning approaches. The proposed BERTCapsules approach consists of a combination of BERT and Capsule models. In this approach, BERT was used for Instance representation, and Capsule Structure was used to learn the extracted graphs. Digikala dataset, including ten domains with both positive and negative polarity, was used to evaluate this approach. The evaluation of the BERTCaps model achieved an accuracy of 0.9712 in sentiment classification binary classification and 0.8509 in domain classification .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
