BERTCaps: BERT Capsule for Persian Multi-Domain Sentiment Analysis
This work addresses sentiment analysis for Persian across multiple domains, but it is incremental as it adapts existing deep learning methods to a specific language and dataset.
The paper tackled the problem of multidomain sentiment analysis for Persian text by proposing BERTCaps, a combination of BERT and Capsule models, achieving an accuracy of 0.9712 in sentiment classification and 0.8509 in domain classification on the Digikala dataset.
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 .