---
title: "SemEval-2015 Task 10 — Rosenthal 2015"
description: "This paper reports on the third edition of the SemEval shared task on Twitter sentiment."
authors: ["Sara Rosenthal", "Preslav Nakov", "Svetlana Kiritchenko", "Saif Mohammad", "Alan Ritter", "Veselin Stoyanov"]
year: 2015
venue: "Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)"
source: "https://aclanthology.org/S15-2078/"
doi: "10.18653/v1/s15-2078"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/rosenthal-2015-semeval-2015-task-10-sentiment/"
---
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# SemEval-2015 Task 10: Sentiment Analysis in Twitter

**Authors:** [Sara Rosenthal](https://md.research.elamur.ai/free/en/authors/sara-rosenthal/), [Preslav Nakov](https://md.research.elamur.ai/free/en/authors/preslav-nakov/), [Svetlana Kiritchenko](https://md.research.elamur.ai/free/en/authors/svetlana-kiritchenko/), [Saif Mohammad](https://md.research.elamur.ai/free/en/authors/saif-mohammad/), [Alan Ritter](https://md.research.elamur.ai/free/en/authors/alan-ritter/), [Veselin Stoyanov](https://md.research.elamur.ai/free/en/authors/veselin-stoyanov/) · **Year:** 2015 · **Venue:** Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/S15-2078/) · [PDF](https://aclanthology.org/S15-2078.pdf) · [DOI 10.18653/v1/s15-2078](https://doi.org/10.18653/v1/s15-2078) · [OpenAlex](https://openalex.org/W2467186984)

**Citations:** 367 citations (openalex-snapshot · 2026-10-14) · [123](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
This paper reports on the third edition of the SemEval shared task on Twitter sentiment. The organizers kept two established subtasks: phrase-level polarity in context (A) and whole-message polarity (B). They added three new ones: polarity toward a given topic in one tweet (C), the aggregate sentiment toward a topic across a set of tweets on a five-level scale (D), and real-valued prior polarity scores for Twitter terms (E).

Tweets about automatically detected trending named entities were gathered through the Twitter streaming API. Messages without sentiment-bearing words were filtered out using SentiWordNet, and the rest were labeled on Mechanical Turk. For subtask E, terms drawn from the Sentiment140 and Hashtag Sentiment corpora were annotated manually.

41 teams took part. Most systems relied on supervised learning with lexicon-derived features. In subtask E, the best system (INESC-ID) combined word embeddings with regression and only narrowly beat a simple lexicon baseline.

The task matters because it set shared benchmarks and moved the field from per-tweet classification toward topic-level quantification.

## Key points
- Participation: 41 teams overall (46 in 2014, 44 in 2013).
  - Subtask B drew 40 teams and A drew 11.
  - The new subtasks C, D and E drew 6, 7 and 10 teams.
- Data filtering: a word counted as sentiment-bearing if at least one of its SentiWordNet 3.0 senses had a positive or negative score above 0.3.
- Annotation quality controls:
  - Each tweet was labeled by five Turkers.
  - Workers needed an approval rate above 95% and at least 50 approved HITs.
- Subtask E data and results:
  - It had a 200-item trial set, a 1,315-item test set and no training data.
  - Kendall's τ was the official metric.
  - INESC-ID scored 0.6251, against 0.5842 for the Sentiment140/Hashtag lexicon baseline.
  - lsislif had the best Spearman's ρ (0.8202).
- Sarcasm: on the 75 sarcastic test tweets, the top score was KLUEless with F1 = 39.26.

## Interesting ideas
- **Lexicon-based pre-filtering for class balance.** Discarding tweets that contain no words from a sentiment lexicon reduces the dominance of the neutral class before costly annotation. It is a cheap trick for any skewed crowdsourcing effort, though it introduces some lexicon bias.
- **One annotation pass for several tasks.** Phrase spans, message polarity and topic polarity were all collected in a single HIT. This makes it possible to build several aligned datasets for roughly the cost of one.
- **Intrinsic evaluation of lexicons via ranking.** Automatically built lexicons are scored by rank correlation (Kendall's τ) against manual polarity scores. This tests the lexicon directly rather than through a downstream classifier, and the approach transfers well to lexicons for other languages, including Ukrainian.
- **Quantification instead of classification.** Subtask D asks for the overall sentiment distribution toward a topic rather than labels for individual tweets. This matches what applications such as marketing or political analysis actually need, and the organizers propose extending it to ordinal quantification on a five-point scale.
- **A transparent, fallback-based baseline.** The subtask E baseline looks up a term in Sentiment140 first, falls back to Hashtag Sentiment, and assigns a neutral 0 to unknown terms. Negated phrases use the lexicons' negated entries. This simple, reproducible design is a useful reference point for judging whether learned systems add real value.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

123 papers of our library cite it (of the 140884 whose bibliographies we read); 6 of them have a page here, 117 not yet.

- [Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora](https://md.research.elamur.ai/free/en/papers/hamilton-2016-inducing-domain-specific-sentiment-lexicons/)
- [A Retrospective Analysis of the Fake News Challenge Stance-Detection Task](https://md.research.elamur.ai/free/en/papers/hanselowski-2018-retrospective-analysis-fake-news-challenge/)
- [SemEval-2016 Task 6: Detecting Stance in Tweets](https://md.research.elamur.ai/free/en/papers/mohammad-2016-semeval-2016-task-6-detecting/)
- [SemEval-2016 Task 4: Sentiment Analysis in Twitter](https://md.research.elamur.ai/free/en/papers/nakov-2016-semeval-2016-task-4-sentiment/)
- [SemEval-2016 Task 5: Aspect Based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/pontiki-2016-semeval-2016-task-5-aspect/)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://md.research.elamur.ai/free/en/papers/rosenthal-2017-semeval-2017-task-4-sentiment/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{rosenthal2015semeval,
  title = {{SemEval-2015 Task 10: Sentiment Analysis in Twitter}},
  author = {Rosenthal, Sara and Nakov, Preslav and Kiritchenko, Svetlana and Mohammad, Saif and Ritter, Alan and Stoyanov, Veselin},
  booktitle = {Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)},
  year = {2015},
  doi = {10.18653/v1/s15-2078},
  url = {https://doi.org/10.18653/v1/s15-2078}
}
```

- [← previous Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems](https://md.research.elamur.ai/free/en/papers/wen-2015-semantically-conditioned-lstm-based-natural/)
- [next → SemEval-2015 Task 12: Aspect Based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/pontiki-2015-semeval-2015-task-12-aspect/)
- [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/page/2/)

## References

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