README.md

---
dataset_info:
- config_name: binary
  features:
  - name: id
    dtype: string
  - name: review
    dtype: string
  - name: sentiment
    dtype: int64
  splits:
  - name: train
    num_bytes: 58962251
    num_examples: 23445
  - name: validation
    num_bytes: 8684098
    num_examples: 2939
  - name: test
    num_bytes: 8823050
    num_examples: 2955
  download_size: 49890837
  dataset_size: 76469399
- config_name: ternary
  features:
  - name: id
    dtype: string
  - name: review
    dtype: string
  - name: sentiment
    dtype: int64
  splits:
  - name: train
    num_bytes: 86258820
    num_examples: 34749
  - name: validation
    num_bytes: 12575768
    num_examples: 4348
  - name: test
    num_bytes: 12734928
    num_examples: 4340
  download_size: 72750831
  dataset_size: 111569516
configs:
- config_name: binary
  data_files:
  - split: train
    path: binary/train-*
  - split: validation
    path: binary/validation-*
  - split: test
    path: binary/test-*
- config_name: ternary
  data_files:
  - split: train
    path: ternary/train-*
  - split: validation
    path: ternary/validation-*
  - split: test
    path: ternary/test-*
license: cc
task_categories:
- text-classification
language:
- 'no'
pretty_name: NoReC_document
size_categories:
- 10K<n<100K
---

# Dataset Card for NoReC_document

<!-- Provide a quick summary of the dataset. -->
Document-level polarity classification of Norwegian full-text reviews across mixed domains.


## Dataset Details

This is a dataset for document-level sentiment classification in Norwegian, derived from the Norwegian Review Corpus: [NoReC](https://huggingface.co/datasets/ltg/norec). We here provide two simplified versions of NoReC where the original six-point numerical ratings have been mapped to a reduced set of categorical classes: _positive_ and _negative_ for the **binary** version, and _positive_, _fair_, and _negative_ for the larger **ternary** version (more details below). This reduces the problem of class imbalance and data scarcity when trying to predict the six-point ratings directly. 

- **Curated by:** The underlying NoReC data was created as part of the [SANT](https://www.mn.uio.no/ifi/english/research/projects/sant/) project (Sentiment Analysis for Norwegian Text), coordinated by the [Language Technology Group](https://www.mn.uio.no/ifi/english/research/groups/ltg/) (LTG) at the University of Oslo, in collaboration with the Norwegian Broadcasting Corporation (NRK), Schibsted Media Group and Aller Media.
- **Funded by:** The [SANT](https://www.mn.uio.no/ifi/english/research/projects/sant/) project 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 (Bokmål and Nynorsk)
- **License:** The data is distributed under a Creative Commons Attribution-NonCommercial licence (CC BY-NC 4.0), access the full license text here: https://creativecommons.org/licenses/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. 
- **Paper:**  [A paper by Velldal et al. at LREC 2018](http://www.lrec-conf.org/proceedings/lrec2018/pdf/851.pdf) describes the (initial release of the) underlying NoReC data in more detail.


## Uses

<!-- Address questions around how the dataset is intended to be used. -->

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


## 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. -->

Each instance in the data has the following three fields:
- 'id': a uniqe document identifier
- 'review': the actual review text 
- 'sentiment': a numerical class label indicating polarity, which can have one of three values:
    - 0 = negative
    - 1 = positive
    - 2 = fair (only available in the ternary version)

In terms of the mapping from the six-point numerical ratings in the underlying [NoReC](https://huggingface.co/datasets/ltg/norec) dataset, 'negative' (0) here corresponds to ratings of 1–3, 'positive' (0) corresponds to ratings of 5–6, while 'fair' corresponds to ratings of 4. In the binary version of NoReC_document, reviews with a rating of 4 are excluded.

The data comes with pre-defined train/dev/test splits, inherited from NoReC.

## Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

The document-level labels are derived from the [Norwegian Review Corpus](https://huggingface.co/datasets/ltg/norec) (NoReC), which contains over 43K 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.

The numerical ratings of the underlying NoReC dataset were provided by the original review authors, i.e. these are the ratings assigned by professional journalists in the published versions of the review articles.


#### 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.


### Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical 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. -->

```
@InProceedings{VelOvrBer18,
  author = {Erik Velldal and Lilja {\O}vrelid and Eivind Alexander Bergem and  Cathrine Stadsnes and Samia Touileb and Fredrik J{\o}rgensen},
  title = {{NoReC}: The {N}orwegian {R}eview {C}orpus},
  booktitle = {Proceedings of the 11th edition of the 
               Language Resources and Evaluation Conference},
  year = {2018},
  address = {Miyazaki, Japan},
  pages = {4186--4191}
}
```

## Dataset Card Authors

Vladislav Mikhailov and Erik Velldal

## Dataset Card Contact

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