Determines the overall sentiment polarity of an entire tweet, addressing class imbalance, slang, and informal social media text. Use when the user wants to benchmark on Twitter2015-test, or asks about evaluating this task. Reports macro-averaged F1.
Scanned 9/11/2026
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---
name: message-level-polarity-eval
description: Determines the overall sentiment polarity of an entire tweet, addressing class imbalance, slang, and informal social media text. Use when the user wants to benchmark on Twitter2015-test, or asks about evaluating this task. Reports macro-averaged F1.
metadata:
skill_kind: dataset_eval
source_arxiv: 1912.02387
bibtex_key: rosenthal2015semeval
confidence: high
---
# message-level-polarity-eval
> SemEval-2015 Task 10: Sentiment Analysis in Twitter — Rosenthal et al. (2015) (SemEval-2015 / arXiv:1912.02387, 2015)
## What this evaluates
Determines the overall sentiment polarity of an entire tweet, addressing class imbalance, slang, and informal social media text.
## Datasets
- **Twitter2015-test** — total ?; splits: test (-1)
## Metrics
- `macro-averaged F1` **(primary)** — range: [0, 1]
- Harmonic mean of precision and recall averaged across all classes (positive, negative, neutral).
## Input / output format
**Input**: A full tweet.
**Output**: Sentiment polarity label (positive, negative, or neutral).
## Scoring recipe
```python
prec = rec = 0
for class in ['pos', 'neg', 'neu']:
prec += precision(y_true, y_pred, class)
rec += recall(y_true, y_pred, class)
macro_prec = prec / 3
macro_rec = rec / 3
f1 = 2 * (macro_prec * macro_rec) / (macro_prec + macro_rec)
```
## Common pitfalls
- Baseline majority class F1 is ~30.3%, so models must significantly outperform simple majority voting.
- Sarcastic tweets are a subset of the test set and evaluated separately; models trained on non-sarcastic data often degrade on this subset.
## Evidence (verbatim from paper)
> The results for subtask B are shown in Table 11. ... with an F1 of 64.84, unitn with 64.59, lislif with 64.27, and INESC-ID with 64.17.
## Citation
```bibtex
@misc{rosenthal2015semeval,
title={SemEval-2015 Task 10: Sentiment Analysis in Twitter},
author={Rosenthal et al. (2015)},
year={2015},
note={SemEval-2015 / arXiv:1912.02387}
}
```
- arXiv: 1912.02387
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