Summary of Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (grus) Model, by Wei Xu et al.
Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model
by Wei Xu, Jianlong Chen, Zhicheng Ding, Jinyin Wang
First submitted to arxiv on: 26 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 The paper proposes a new approach to text sentiment analysis and classification using bidirectional gated recurrent units (GRUs) models. The GRUs model is trained on a dataset of texts labeled with six sentiment categories, and the accuracy of the validation set increases from 85% to 93% after training. The loss value decreases from 0.7 to 0.1, indicating that the model improves its performance over time. The confusion matrix shows that the model has good generalization ability and classification effect, with an accuracy of 94.8%, precision of 95.9%, recall of 99.1%, and F1 score of 97.4%. This approach can effectively classify text emotions and has potential applications in natural language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to analyze and understand how people feel about things they read or hear. It uses special computer models called GRUs to sort texts into happy, sad, angry, scared, surprised, and neutral categories. The model gets better at predicting the correct category as it trains on more data. This helps improve its accuracy from 85% to 93%. The results show that this approach is very good at understanding text emotions, with an accuracy of 94.8%, precision of 95.9%, recall of 99.1%, and F1 score of 97.4%. |
Keywords
» Artificial intelligence » Classification » Confusion matrix » F1 score » Generalization » Natural language processing » Precision » Recall
