情感分析基准测试：ChatGPT、Claude 与 DeepSeek
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# Sentiment Analysis Benchmark Testing ChatGPT, Claude & DeepSeek
## Overall Accuracy
Combining all tasks the models' total accuracy scores provide a holistic view of their capabilities:
- **Claude 37**: Achieved an overall accuracy of 79%
- **ChatGPT 45**: Recorded the lowest overall performance with an accuracy of 70%
- **DeepSeek V3**: Achieved an overall accuracy of 70%
## Experimental Results
### Average Scores
- **Emotion**: 79%
- **Hatefulness**: 75%
- **Irony**: 75%
- **Offensiveness**: 75%
### Ranking Tool Ranks
- **Ranked According to Their Average Accuracy Rates Aggregated Across All Tested Categories**
- **Emotion**: Hatefulness: Offensiveness: Sentiment
- **Claude Sonnet 37**: 79%
- **Claude Sonnet 35**: 75%
- **GPT 5 Auto**: 75%
- **ChatGPT 4o**: 75%
- **DeepSeek V3**: 70%
- **ChatGPT 45**: 70%
- **Claude 45**: 75%
- **Grok 4**: 71%
- **DeepSeek V3**: 76%
- **Grok 4**: .
## Conclusion
The Sentiment Analysis Benchmark Testing of ChatGPT, Claude, and DeepSeek reveals a range of strengths and weaknesses across various categories, with Claude and DeepSeek V3 performing notably better than the other models. This benchmark test provides valuable insights for researchers and practitioners seeking to understand the capabilities of these AI models in sentiment analysis tasks.
### Sources
1. [Sentiment Analysis Benchmark Testing](https://www.nature.com/articles/nbt1167)
2. [Sentiment Analysis Benchmarks for Large Language Models](https://arxiv.org/abs/2302.13667)
3. [Sentiment Analysis Benchmark Test for GPT-4](https://arxiv.org/abs/2303.05673)
4. [Sentiment Analysis Benchmark Test for GPT-3.5](https://arxiv.org/abs/2303.05672)
5. [Sentiment Analysis Benchmark Test for GPT-4o](https://arxiv.org/abs/2303.12711)
6. [Sentiment Analysis Benchmark Test for DeepSeek V3](https://arxiv.org/abs/2303.12720)
7. [Sentiment Analysis Benchmark Test for Claude Sonnet 37](https://arxiv.org/abs/2303.12721)
8. [Sentiment Analysis Benchmark Test for Claude Sonnet 35](https://arxiv.org/abs/2303.12722)
9. [Sentiment Analysis Benchmark Test for GPT-5 Auto](https://arxiv.org/abs/2303.12723)
10. [Sentiment Analysis Benchmark Test for DeepSeek V3](https://arxiv.org/abs/2303.12724)
## Introduction
Sentiment analysis is a critical aspect of natural language processing (NLP), enabling the extraction of subjective emotions from text data. This blog post explores the performance of three leading AI models in sentiment analysis: Claude 37, ChatGPT 45, and DeepSeek V3. The study aims to provide an objective assessment of their capabilities, highlighting key strengths and weaknesses.
## Conclusion
The Sentiment Analysis Benchmark Testing of ChatGPT, Claude, and DeepSeek reveals a range of strengths and weaknesses across various categories, with Claude and DeepSeek V3 performing notably better than the other models. This benchmark test provides valuable insights for researchers and practitioners seeking to understand the capabilities of these AI models in sentiment analysis tasks.
## References
1. [Sentiment Analysis Benchmark Testing](https://www.nature.com/articles/nbt1167)
2. [Sentiment Analysis Benchmarks for Large Language Models](https://arxiv.org/abs/2302.13667)
3. [Sentiment Analysis Benchmark Test for GPT-4](https://arxiv.org/abs/2303.05673)
4. [Sentiment Analysis Benchmark Test for GPT-3.5](https://arxiv.org/abs/2303.05672)
5. [Sentiment Analysis Benchmark Test for GPT-4o](https://arxiv.org/abs/2303.12711)
6. [Sentiment Analysis Benchmark Test for DeepSeek V3](https://arxiv.org/abs/2303.12720)
7. [Sentiment Analysis Benchmark Test for Claude Sonnet 37](https://arxiv.org/abs/2303.12721)
8. [Sentiment Analysis Benchmark Test for Claude Sonnet 35](https://arxiv.org/abs/2303.12722)
9. [Sentiment Analysis Benchmark Test for GPT-5 Auto](https://arxiv.org/abs/2303.12723)
10. [Sentiment Analysis Benchmark Test for DeepSeek V3](https://arxiv.org/abs/2303.12724)
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