用BERT与胶囊网络结合提升波斯语多领域情感分析准确率
BERTCaps: BERT Capsule for Persian Multi-Domain Sentiment Analysis
- 融合BERT与胶囊网络，利用文本上下文和结构特征建模
- 在十领域数据集上实现97.12%情感分类准确率
- 适合需要跨领域泛化能力的波斯语情感分析任务
多领域情感分析旨在通过利用领域特定信息估计非结构化文本的情感极性。现有方法普遍难以适用于未参与模型训练的领域。本文提出基于深度学习的波斯语多领域情感分析新方法BERTCaps。该方法结合BERT进行实例表征，利用胶囊结构学习提取的图特征。实验基于包含十个领域的Digikala数据集，评估结果显示：情感二分类准确率达0.9712，领域分类准确率为0.8509。
原文摘要 · Abstract (English)
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 .
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