Summary of Relational Graph Convolutional Networks For Sentiment Analysis, by Asal Khosravi et al.
Relational Graph Convolutional Networks for Sentiment Analysis
by Asal Khosravi, Zahed Rahmati, Ali Vefghi
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 proposes a novel approach to sentiment analysis using Relational Graph Convolutional Networks (RGCNs) that leverages pre-trained language models such as BERT and RoBERTa. By representing data points as nodes in a graph, RGCNs can capture complex relationships between entities, offering interpretability and flexibility for sentiment analysis tasks. The proposed approach is evaluated on product reviews from Amazon and Digikala datasets, demonstrating its effectiveness in capturing relational information for sentiment analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers to analyze how people feel about things they write online. It’s hard to understand what people mean when they say nice or nasty things, but this computer program can get it right most of the time. The special trick is that it looks at all the words and connections between them, like friends on social media. This helps it figure out if someone likes or dislikes something. They tested their way by looking at what people wrote about products on Amazon and another website. It worked pretty well! |
Keywords
» Artificial intelligence » Bert
