Summary of Optimizing Transformer Based on High-performance Optimizer For Predicting Employment Sentiment in American Social Media Content, by Feiyang Wang et al.
Optimizing Transformer based on high-performance optimizer for predicting employment sentiment in American social media content
by Feiyang Wang, Qiaozhi Bao, Zixuan Wang, Yanlin Chen
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The article proposes an improved Transformer model optimized using swarm intelligence algorithms for predicting emotions in employment-related text content on American social media. The model is trained on preprocessed and vectorized text data, showing a significant increase in accuracy from 49.27% to 82.83% during training. The model’s performance is evaluated through confusion matrices, demonstrating high accuracy (86.15%) on the training set and strong generalization ability with an accuracy difference of only 3.24% between the training and test sets. Additionally, the model exhibits good classification accuracy, sensitivity, specificity, and area under the curve (AUC) in sentiment analysis, with a Kappa coefficient of 0.66 and F-measure of 0.80. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The article improves a Transformer model for recognizing emotions in social media text about employment. It uses special computer algorithms to make the model better. The researchers tested the model on lots of text data and found it got really good at understanding people’s feelings! The model is useful because it can help us understand what people are talking about online and make decisions that will improve working conditions. |
Keywords
» Artificial intelligence » Auc » Classification » Generalization » Transformer
