Measures the change in sentiment trend towards a specific topic over time or across datasets, requiring temporal or comparative analysis. Use when the user wants to benchmark on Twitter2015-test, or asks about evaluating this task. Reports avgDiff.
Scanned 9/11/2026
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---
name: topic-trend-detection-eval
description: Measures the change in sentiment trend towards a specific topic over time or across datasets, requiring temporal or comparative analysis. Use when the user wants to benchmark on Twitter2015-test, or asks about evaluating this task. Reports avgDiff.
metadata:
skill_kind: dataset_eval
source_arxiv: 1912.02387
bibtex_key: rosenthal2015semeval
confidence: high
---
# topic-trend-detection-eval
> SemEval-2015 Task 10: Sentiment Analysis in Twitter — Rosenthal et al. (2015) (SemEval-2015 / arXiv:1912.02387, 2015)
## What this evaluates
Measures the change in sentiment trend towards a specific topic over time or across datasets, requiring temporal or comparative analysis.
## Datasets
- **Twitter2015-test** — total ?; splits: test (-1)
## Metrics
- `avgDiff` **(primary)** — range: other
- Average difference in sentiment scores across time periods or datasets for a given topic. Systems are ranked by this metric.
- `avgLevelDiff` — range: other
- Average level difference metric used to rank systems; yields identical rankings to avgDiff.
## Input / output format
**Input**: Tweets associated with a topic, ordered or grouped by time/context.
**Output**: Trend score or direction for the topic.
## Scoring recipe
```python
# avgDiff and avgLevelDiff are provided by the task organizers
# Systems are ranked by avgDiff
rank = sort_by(avgDiff, descending)
# avgLevelDiff yields the same ranking
```
## Common pitfalls
- Only three teams improved over the baseline, indicating high difficulty.
- Trend detection is closely related to topic-level polarity; solving C is a prerequisite for D, and rankings differ from C.
## Evidence (verbatim from paper)
> The results for subtask D are shown in Table 13. ... Finally, note that avgDiff and avgLevelDiff yielded the same rankings.
## 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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