# Sentiment Analysis (Sentence-Level) on Amazon-2 v1 (test)

Source: [Sentiment Analysis (Sentence-Level) on Amazon-2 v1 (test)](<https://www.sota2.com/research/sota/sentiment-analysis-sentence-level-on-amazon-2-v1-test>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-26T05:27:38.410Z

Evaluation of sentence-level sentiment analysis performance on the test split of the Amazon-2 v1 dataset, measured using Accuracy.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Sentiment Analysis (Sentence-Level) |
| Dataset (including any stated split) | Amazon-2 v1 (test) |
| Metric | Accuracy |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 5 of 5 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [Accuracy](<https://www.sota2.com/research/sota/sentiment-analysis-sentence-level-on-amazon-2-v1-test.md?metric=Accuracy>): Higher is better; unit: PERCENT.

## Recorded results

| Method | Accuracy | Result date | Report paper |
| --- | --- | --- | --- |
| RoBERTa + SKEP | 97.56 | Not recorded | 68fa67921e209131cea1718e |
| Previous SOTA | 97.37 | Not recorded | 68fa67921e209131cea1718e |
| RoBERTa | 97.33 | Not recorded | 68fa67921e209131cea1718e |
| RoBERTa + SKEP | 96.94 | Not recorded | 68fa67921e209131cea1718e |
| RoBERTa | 96.61 | Not recorded | 68fa67921e209131cea1718e |

### RoBERTa + SKEP

Accuracy: 97.56. Result ID: 698d3b7e8b517eb4331ff68c.

[Reporting paper](<https://www.sota2.com/research/paper/68fa67921e209131cea1718e.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa67921e209131cea1718e.md>)

[Implementation](<https://github.com/baidu/Senta>)

Recorded evaluation configuration: {"Model Size":"large"}. This record does not assert independent reproduction.

### Previous SOTA

Accuracy: 97.37. Result ID: 698d3b7e8b517eb4331ff688.

[Reporting paper](<https://www.sota2.com/research/paper/68fa67921e209131cea1718e.md>)

Recorded evaluation configuration: Not recorded. This record does not assert independent reproduction.

### RoBERTa

Accuracy: 97.33. Result ID: 698d3b7e8b517eb4331ff68b.

[Reporting paper](<https://www.sota2.com/research/paper/68fa67921e209131cea1718e.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa66131e209131cea0e18b.md>)

[Implementation](<https://github.com/pytorch/fairseq>)

Recorded evaluation configuration: {"Model Size":"large"}. This record does not assert independent reproduction.

### RoBERTa + SKEP

Accuracy: 96.94. Result ID: 698d3b7e8b517eb4331ff68a.

[Reporting paper](<https://www.sota2.com/research/paper/68fa67921e209131cea1718e.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa67921e209131cea1718e.md>)

[Implementation](<https://github.com/baidu/Senta>)

Recorded evaluation configuration: {"Model Size":"base"}. This record does not assert independent reproduction.

### RoBERTa

Accuracy: 96.61. Result ID: 698d3b7e8b517eb4331ff689.

[Reporting paper](<https://www.sota2.com/research/paper/68fa67921e209131cea1718e.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa66131e209131cea0e18b.md>)

[Implementation](<https://github.com/pytorch/fairseq>)

Recorded evaluation configuration: {"Model Size":"base"}. This record does not assert independent reproduction.

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