# weighted CapsuleNet networks for Persian multi-domain sentiment analysis

arXiv: https://arxiv.org/abs/2306.17068 · HTML: https://mlsanity.com/paper/2306.17068/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos](https://mlsanity.com/paper/1810.11215/) (2018)
- [A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1510.03820/) (2015)
- [Text Classification using Capsules](https://mlsanity.com/paper/1808.03976/) (2018)
- [Cost-Sensitive Convolution based Neural Networks for Imbalanced Time-Series Classification](https://mlsanity.com/paper/1801.04396/) (2018)
- [Arabic aspect based sentiment analysis using bidirectional GRU based models](https://mlsanity.com/paper/2101.10539/) (2021)
- [The Challenges of Persian User-generated Textual Content: A Machine Learning-Based Approach](https://mlsanity.com/paper/2101.08087/) (2021)
- [Sentylic at IEST 2018: Gated Recurrent Neural Network and Capsule Network Based Approach for Implicit Emotion Detection](https://mlsanity.com/paper/1809.01452/) (2018)

## Built on by (most cited first, 1 of 1)

- [Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Models](https://mlsanity.com/paper/2509.05719/) (2025)

## Abstract (arXiv)

Sentiment classification is a fundamental task in natural language processing, assigning one of the three classes, positive, negative, or neutral, to free texts. However, sentiment classification models are highly domain dependent; the classifier may perform classification with reasonable accuracy in one domain but not in another due to the Semantic multiplicity of words getting poor accuracy. This article presents a new Persian/Arabic multi-domain sentiment analysis method using the cumulative weighted capsule networks approach. Weighted capsule ensemble consists of training separate capsule networks for each domain and a weighting measure called domain belonging degree (DBD). This criterion consists of TF and IDF, which calculates the dependency of each document for each domain separately; this value is multiplied by the possible output that each capsule creates. In the end, the sum of these multiplications is the title of the final output, and is used to determine the polarity. And the most dependent domain is considered the final output for each domain. The proposed method was evaluated using the Digikala dataset and obtained acceptable accuracy compared to the existing approaches. It achieved an accuracy of 0.89 on detecting the domain of belonging and 0.99 on detecting the polarity. Also, for the problem of dealing with unbalanced classes, a cost-sensitive function was used. This function was able to achieve 0.0162 improvements in accuracy for sentiment classification. This approach on Amazon Arabic data can achieve 0.9695 accuracies in domain classification.