BERTCaps: BERT Capsule for Persian Multi-Domain Sentiment Analysis

TL;DR

This paper introduces BERTCapsules, a deep learning model combining BERT and Capsule networks for Persian multidomain sentiment analysis, achieving high accuracy across multiple domains.

Contribution

It presents a novel BERTCapsules model specifically designed for Persian multidomain sentiment analysis, integrating BERT and Capsule networks for improved domain adaptability.

Findings

Achieved 97.12% accuracy in sentiment classification

Achieved 85.09% accuracy in domain classification

Effective across ten diverse domains

Abstract

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 .