---
language:
- nb
size_categories:
- 10K<n<100K
task_categories:
- text-classification
pretty_name: NoReC_sentence
dataset_info:
- config_name: binary
  features:
  - name: id
    dtype: string
  - name: review
    dtype: string
  - name: sentiment
    dtype: int64
  splits:
  - name: train
    num_bytes: 504530
    num_examples: 3894
  - name: validation
    num_bytes: 90797
    num_examples: 701
  - name: test
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    num_examples: 583
  download_size: 419034
  dataset_size: 671750
- config_name: default
  features:
  - name: id
    dtype: string
  - name: review
    dtype: string
  - name: sentiment
    dtype:
      class_label:
        names:
          '0': negative
          '1': neutral
          '2': positive
  splits:
  - name: train
    num_bytes: 850007
    num_examples: 7973
  - name: validation
    num_bytes: 153447
    num_examples: 1411
  - name: test
    num_bytes: 129270
    num_examples: 1181
  download_size: 740756
  dataset_size: 1132724
configs:
- config_name: binary
  data_files:
  - split: train
    path: binary/train-*
  - split: validation
    path: binary/validation-*
  - split: test
    path: binary/test-*
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---
# Dataset Card for NoReC_sentence

<!-- Provide a quick summary of the dataset. -->

Sentence-level polarity classification of Norwegian sentences from reviews across mixed domains.

## Dataset Details

### Dataset Description

This is a dataset for sentence-level sentiment classification in Norwegian, derived from the the fine-grained annotations of [NoReC_fine](https://github.com/ltgoslo/norec_fine). We here provide a version where the annotations have been aggregated at the sentence-level, by only keeping sentences that contain sentiment annotations of either positive or negative polarity (but not both), in addition to sentences having no sentiment at all (neutral).
Note that sentences that contained mixed polarity are excluded. The data comes with pre-defined train/dev/test splits. It can be used for either binary (positive vs negative) or three-way classificaton, depending on whether sentences with the neutral label is considered or not.

- **Curated by:** The [SANT](https://www.mn.uio.no/ifi/english/research/projects/sant/) project (Sentiment Analysis for Norwegian Text) at the [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) (LTG) at the University of Oslo
- **Funded by:** The [SANT](https://www.mn.uio.no/ifi/english/research/projects/sant/) is funded by the [Research Council of Norway](https://www.forskningsradet.no/en/) (NFR grant number 270908).
- **Shared by:** The [SANT](https://www.mn.uio.no/ifi/english/research/projects/sant/) project (Sentiment Analysis for Norwegian Text) at the [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) (LTG) at the University of Oslo
- **Language(s) (NLP):** Norwegian (Nokmål and Nynorsk)
- **License:** The data is distributed under a [Creative Commons Attribution-NonCommercial licence](https://creativecommons.org/licenses/by-nc/4.0/) (CC BY-NC 4.0). The licence is motivated by the need to block the possibility of third parties redistributing the orignal reviews for commercial purposes. Note that machine learned models, extracted lexicons, embeddings, and similar resources that are created on the basis of NoReC are not considered to contain the original data and so can be freely used also for commercial purposes despite the non-commercial condition.

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** [https://github.com/ltgoslo/norec_sentence](https://github.com/ltgoslo/norec_sentence)
- **Paper:** The underlying NoReC_fine dataset is described in the paper [A Fine-Grained Sentiment Dataset for Norwegian](https://aclanthology.org/2020.lrec-1.618/) by Øvrelid et al., published at LREC 2020. The aggregation to the sentence-level was first described in [Large-Scale Contextualised Language Modelling for Norwegian](https://aclanthology.org/2021.nodalida-main.4/) by Kutuzov et al. at NoDaLiDa 2021.

## Uses

The data is intended to be used for training and testing models for Norwegian sentence-level classification of polarity, either binary (positive / negative) or ternary (positive / negative / neutral).

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

[More Information Needed]

## Dataset Creation

### Curation Rationale

The aggragated annotations of NoReC_sentence are primarily intended for benchmarking purposes.

### Source Data

The sentence-level annotations are aggregated from the NoReC_fine dataset, which in turn comprises a subset of the documents in the [Norwegian Review Corpus](https://github.com/ltgoslo/norec) (NoReC), which contains full-text professional reviews collected from major Norwegian news sources and cover a range of different domains, including literature, movies, video games, restaurants, music and theater, in addition to product reviews across a range of categories. The review articles NoReC were originally donated by the media partners in the SANT project; the Norwegian Broadcasting Corporation (NRK), Schibsted Media Group and Aller Media. The data comprises reviews extracted from eight different Norwegian news sources: Dagbladet, VG, Aftenposten, Bergens Tidende, Fædrelandsvennen, Stavanger Aftenblad, DinSide.no and P3.no. In terms of publishing date the reviews of NoReC mainly cover the time span 2003–2019, although it also includes a handful of reviews dating back as far as 1998.

### Annotators

The original annotations of NoReC_fine that the sentence-level labels here are derived from, were originally created by hired annotators who were all BSc- or MSc-level students in the Language Technology study program at the Department of informatics, University of Oslo. 

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

The data does not contain information considered personal or sensitive.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Results obtained on this data might not generalize to texts from other domains or genres. Any biases in the sentiments expressed by the original review authors may carry over to models trained on this data.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@InProceedings{KutBarVel21,
  author = {Andrey Kutuzov and Jeremy Barnes and Erik Velldal and Lilja {\O}vrelid and Stephan Oepen}, 
  title = {Large-Scale Contextualised Language Modelling for Norwegian},
  booktitle = {{Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa 2021)}},
  year = 2021
}

@InProceedings{OvrMaeBar20,
  author = {Lilja {\O}vrelid and Petter M{\ae}hlum and Jeremy Barnes and Erik Velldal},
  title = {A Fine-grained Sentiment Dataset for {N}orwegian},
  booktitle = {{Proceedings of the 12th Edition of the Language Resources and Evaluation Conference}},
  year = 2020,
  address = "Marseille, France, 2020"
}
```

**APA:**

[More Information Needed]


## Dataset Card Authors

Vladislav Mikhailov and Erik Velldal

## Dataset Card Contact

vladism@ifi.uio.no and erikve@ifi.uio.no