Summary of Bertcaps: Bert Capsule For Persian Multi-domain Sentiment Analysis, by Mohammadali Memari et al.
BERTCaps: BERT Capsule for Persian Multi-Domain Sentiment Analysis
by Mohammadali Memari, Soghra Mikaeyl Nejad, Amir Parsa Rabiei, Mehrshad Eisaei, Saba Hesaraki
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel deep learning-based approach for Persian multidomain sentiment analysis (SA). The proposed method, called BERTCapsules, combines the strengths of BERT and Capsule models to estimate the polarity of unstructured text by exploiting domain-specific information. In this approach, BERT is used for instance representation, and Capsule Structure is employed to learn extracted graphs. The BERTCaps model is evaluated on the Digikala dataset, which consists of ten domains with both positive and negative polarity. The results show an accuracy of 0.9712 in sentiment classification and 0.8509 in domain classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand how people feel about things using computer science. It’s like trying to figure out if someone is happy or sad based on what they say, but it gets even harder because people might talk differently depending on where they’re from. The new method uses special computers that can learn and understand patterns in language, kind of like how we learn from experiences. They tested this approach on a big dataset with lots of different types of text and found out that it’s really good at figuring out what people are feeling. |
Keywords
* Artificial intelligence * Bert * Classification * Deep learning
