---
title: "SemEval-2016 Task 5: Aspect Based Sentiment Analysis — Pontiki 2016"
description: "This paper reports on the third edition of SemEval's aspect-level sentiment analysis challenge, known as ABSA. 32 references, cited by 375 papers here."
authors: ["Maria Pontiki", "Dimitris Galanis", "Haris Papageorgiou", "Ion Androutsopoulos", "Suresh Manandhar", "Mohammad AL-Smadi", "Mahmoud Al-Ayyoub", "Yanyan Zhao", "Bing Qin", "Orphée De Clercq", "Véronique Hoste", "Marianna Apidianaki", "Xavier Tannier", "Natalia Loukachevitch", "Evgeniy Kotelnikov", "Nuria Bel", "Salud María Jiménez-Zafra", "Gülşen Eryiğit"]
year: 2016
venue: "Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)"
source: "https://aclanthology.org/S16-1002/"
doi: "10.18653/v1/s16-1002"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/pontiki-2016-semeval-2016-task-5-aspect/"
---
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# SemEval-2016 Task 5: Aspect Based Sentiment Analysis

**Authors:** [Maria Pontiki](https://md.research.elamur.ai/free/en/authors/maria-pontiki/), [Dimitris Galanis](https://md.research.elamur.ai/free/en/authors/dimitris-galanis/), [Haris Papageorgiou](https://md.research.elamur.ai/free/en/authors/haris-papageorgiou/), [Ion Androutsopoulos](https://md.research.elamur.ai/free/en/authors/ion-androutsopoulos/), [Suresh Manandhar](https://md.research.elamur.ai/free/en/authors/suresh-manandhar/), [Mohammad AL-Smadi](https://md.research.elamur.ai/free/en/authors/mohammad-al-smadi/), [Mahmoud Al-Ayyoub](https://md.research.elamur.ai/free/en/authors/mahmoud-al-ayyoub/), [Yanyan Zhao](https://md.research.elamur.ai/free/en/authors/yanyan-zhao/), [Bing Qin](https://md.research.elamur.ai/free/en/authors/bing-qin/), [Orphée De Clercq](https://md.research.elamur.ai/free/en/authors/orphee-de-clercq/), [Véronique Hoste](https://md.research.elamur.ai/free/en/authors/veronique-hoste/), [Marianna Apidianaki](https://md.research.elamur.ai/free/en/authors/marianna-apidianaki/), [Xavier Tannier](https://md.research.elamur.ai/free/en/authors/xavier-tannier/), Natalia Loukachevitch, Evgeniy Kotelnikov, [Nuria Bel](https://md.research.elamur.ai/free/en/authors/nuria-bel/), Salud María Jiménez-Zafra and [Gülşen Eryiğit](https://md.research.elamur.ai/free/en/authors/gulsen-eryi-it/) · **Year:** 2016 · **Venue:** Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/S16-1002/) · [PDF](https://aclanthology.org/S16-1002.pdf) · [DOI 10.18653/v1/s16-1002](https://doi.org/10.18653/v1/s16-1002) · [OpenAlex](https://openalex.org/W2465978385)

**Citations:** 1,281 citations (OpenAlex, 2026-10-14) · [375](https://md.research.elamur.ai/free/en/papers/pontiki-2016-semeval-2016-task-5-aspect/cited-by/) in our library, of the 140,884 papers whose bibliographies we read

## Summary
This paper reports on the third edition of SemEval's aspect-level sentiment analysis challenge, known as ABSA. ABSA means detecting which entities and attributes a review talks about and what sentiment is expressed toward each. Earlier editions (2014, 2015) were English-only and sentence-focused. SE-ABSA16 extended the SE-ABSA15 annotation framework in three ways: to seven more languages (Arabic, Chinese, Dutch, French, Russian, Spanish, Turkish), to new domains (cameras, phones, museums, telecom tweets), and to a new text-level subtask that summarises the opinions of a whole review.

The organisers released 39 manually annotated datasets with a shared evaluation procedure and baselines. Participants received 245 runs from 29 teams. Most systems beat the baselines, and runs that used external resources scored above constrained ones. Two contrasts stood out:
- **Detecting aspects** was easier at the text level than at the sentence level.
- **Assigning polarity** was harder at the text level, because reviews often mix sentiments about the same aspect.

The resource matters because it is a multilingual, multi-domain ABSA benchmark built under common guidelines, which enables cross-lingual work.

## Key points
- The task released 19 training and 20 test datasets covering 8 languages and 7 domains. Of these, 25 target sentence-level ABSA and 14 target text-level ABSA. There are 70,790 tuples in total: 47,654 at sentence level and 23,136 at text level (6 languages, 3 domains).
- Subtask 1 (sentence-level) has three slots:
  - Slot1: an aspect category, written as an entity#attribute pair (E#A) from fixed inventories.
  - Slot2: the Opinion Target Expression (OTE), given by character offsets, or "null" when the target is implicit.
  - Slot3: polarity, one of positive, negative or neutral.
- Subtask 2 (text-level) outputs {category, polarity} pairs per review and adds a "conflict" label. Subtask 3 (out-of-domain) offered only French museum data and received no submissions.
- Teams could submit constrained runs (provided training data only) and unconstrained runs (any external resources). English drew by far the most interest, with 27 teams and 156 runs.
- The best Slot1 and Slot2 scores were often similar. Joint {Slot1, Slot2} scores were clearly lower, and en/rest gave the highest results overall.

## Interesting ideas
- **Linked opinion tuples:** Each opinion is stored as one record tying category, target span and polarity together. This lets the evaluation reward correct linking, not just isolated labels, and the format would suit other structured extraction tasks.
- **Text-level labels built from sentence-level ones:** Annotators derived review-level labels from sentence-level annotations, picking the dominant sentiment rather than simply aggregating. When no sentiment dominated, they used a "conflict" label. Making aggregation an explicit judgment like this is useful wherever document labels are derived from fragments.
- **Constrained vs. unconstrained tracks:** Running two parallel tracks separates the gains from better modelling from the gains due to extra lexicons or data. This makes leaderboards easier to interpret.
- **Shared guidelines across languages:** Applying one schema across languages and domains makes datasets directly comparable. It also opens the way to cross-lingual or language-agnostic methods, a practical template for building resources in a new language such as Ukrainian.
- **Out-of-domain track:** Test domains were hidden until evaluation began, which gives an honest measure of robustness to unseen domains.

*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/)

Topics: [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/)

## Institutions

- [Centre National de la Recherche Scientifique](https://md.research.elamur.ai/free/en/labs/centre-national-de-la-recherche-scientifique/) — Research institute, Paris, France · 83 works · 59,287 citations
- [Harbin Institute of Technology](https://md.research.elamur.ai/free/en/universities/harbin-institute-of-technology/) — University, Harbin, China · 58 works · 25,250 citations
- [Athens University of Economics and Business](https://md.research.elamur.ai/free/en/universities/athens-university-of-economics-and-business/) — University, Athens, Greece · 14 works · 9,217 citations
- [Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur](https://md.research.elamur.ai/free/en/labs/laboratoire-d-informatique-pour-la-mecanique-et-les-sciences-de-l-ingenieur/) — Research institute, Orsay, France · 18 works · 6,905 citations
- [University of York](https://md.research.elamur.ai/free/en/universities/university-of-york/) — University, York, United Kingdom · 10 works · 6,464 citations
- [Universitat Pompeu Fabra](https://md.research.elamur.ai/free/en/universities/universitat-pompeu-fabra/) — University, Barcelona, Spain · 27 works · 5,604 citations
- [Institute for Language and Speech Processing](https://md.research.elamur.ai/free/en/labs/institute-for-language-and-speech-processing/) — Research institute, Marousi, Greece · 6 works · 5,514 citations
- [Université Paris-Saclay](https://md.research.elamur.ai/free/en/universities/universite-paris-saclay/) — University, Gif-sur-Yvette, France · 26 works · 5,075 citations
- [Université Paris-Sud](https://md.research.elamur.ai/free/en/universities/universite-paris-sud/) — University, Orsay, France · 7 works · 4,930 citations
- [Athena Research and Innovation Center In Information Communication & Knowledge Technologies](https://md.research.elamur.ai/free/en/labs/athena-research-and-innovation-center-in-information-communication-knowledge/) — Research institute, Marousi, Greece · 6 works · 3,438 citations
- [Istanbul Technical University](https://md.research.elamur.ai/free/en/universities/istanbul-technical-university/) — University, Istanbul, Türkiye · 7 works · 2,869 citations
- [Ghent University](https://md.research.elamur.ai/free/en/universities/ghent-university/) — University, Ghent, Belgium · 14 works · 2,565 citations
- [Lomonosov Moscow State University](https://md.research.elamur.ai/free/en/universities/lomonosov-moscow-state-university/) — University, Moscow, Russia · 6 works · 2,378 citations
- [Jordan University of Science and Technology](https://md.research.elamur.ai/free/en/universities/jordan-university-of-science-and-technology/) — University, Irbid, Jordan · 9 works · 1,785 citations

Also on the byline: [Universidad de Jaén](https://ror.org/0122p5f64) · [Vyatka State University](https://ror.org/01anxqg21).

## Cite

Cite as: Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad AL-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, Véronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia Loukachevitch, Evgeniy Kotelnikov, Nuria Bel, Salud María Jiménez-Zafra and Gülşen Eryiğit. 2016. SemEval-2016 Task 5: Aspect Based Sentiment Analysis. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016).

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

```bibtex
@inproceedings{pontiki2016semeval,
  title = {{SemEval-2016 Task 5: Aspect Based Sentiment Analysis}},
  author = {Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad AL-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée {De Clercq}, Véronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia Loukachevitch, Evgeniy Kotelnikov, Nuria Bel, Salud María Jiménez-Zafra and Gülşen Eryiğit},
  booktitle = {Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)},
  year = {2016},
  doi = {10.18653/v1/s16-1002},
  url = {https://doi.org/10.18653/v1/s16-1002}
}
```

- [← previous Pointer Sentinel Mixture Models](https://md.research.elamur.ai/free/en/papers/merity-2016-pointer-sentinel-mixture-models/)
- [next → Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding](https://md.research.elamur.ai/free/en/papers/fukui-2016-multimodal-compact-bilinear-pooling-visual/)
- [↑ back to papers of 2016](https://md.research.elamur.ai/free/en/papers/2016/)

## References

32 references, 6 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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