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SemEval-2017 Task 4: Sentiment Analysis in Twitter

Written for the Research library (claude-opus-5-5) from the paper text. Original — aclanthology.org/S17-2088.pdf

Summary

SemEval-2017 Task 4 is the fifth edition of the shared evaluation on Twitter sentiment analysis. It repeats the five subtasks introduced in 2016:

  • Subtask A: three-way overall tweet polarity.
  • Subtasks B and C: sentiment towards a given topic, on a two-point and a five-point ordinal scale.
  • Subtasks D and E: quantification, i.e., estimating how the tweets on a topic are distributed across sentiment classes, again on two-point and five-point scales.

This edition added two things. Every subtask was also offered in Arabic, a morphologically rich, dialect-heavy language with few topic-level sentiment resources. Participants also received a script for retrieving public profile data about the tweet authors.

The organizers collected tweets on trending topics through the Twitter API, filtered out near-duplicates, and had them labelled on CrowdFlower with quality controls. Test topics did not overlap with training topics.

The task drew 48 teams. Deep learning was the dominant approach among state-of-the-art systems, while attempts to exploit user metadata brought limited benefit. The freely released English and Arabic datasets provide a standard benchmark, especially for topic-oriented and Arabic sentiment analysis.

Key points

  • 48 participating teams; all five subtasks (A–E) were available in both English and Arabic. English training data reused all earlier editions' sets, while new Arabic training data and new test sets for both languages were collected.
  • Data cleaning removed tweets whose bag-of-words cosine similarity exceeded 0.6 and kept only topics with at least 100 remaining tweets. Arabic training tweets date from September–November 2016; test tweets date from December 2016–January 2017.
  • Each tweet was labelled by at least five CrowdFlower annotators, with hidden test questions used to reject unreliable contributors. Annotators were told that topic-level polarity must reflect an opinion about the topic itself, not merely an event related to it.
  • Four teams (SINAI, ECNU, TakeLab, OMAM) tried user information. TakeLab and OMAM saw no gains and dropped it. SINAI built per-user models from the authors' last 200 tweets, applying a user model only when a general word2vec-based SVM classified that user's tweets with accuracy above 0.7.
  • Topics were chosen from trending events, partly shared across the two languages to encourage cross-lingual methods. Examples include Donald Trump, iPhone, Aleppo and the Dakota Access Pipeline.

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

Cited by

289 papers of our library cite it (of the 140673 whose bibliographies we read); 11 of them have a page here, 278 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/s17-2088

@inproceedings{rosenthal2017semeval,
  title = {{SemEval-2017 Task 4: Sentiment Analysis in Twitter}},
  author = {Rosenthal, Sara and Farra, Noura and Nakov, Preslav},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  year = {2017},
  doi = {10.18653/v1/s17-2088},
  url = {https://doi.org/10.18653/v1/s17-2088}
}

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