---
language:
- 'no'
license: cc
size_categories:
- 10K<n<100K
pretty_name: NoReC
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
dataset_info:
  language:
  - 'no'
  license: cc
  size_categories:
  - 10K<n<100K
  pretty_name: NoReC
  configs:
  - config_name: default
    data_files:
    - split: train
      path: data/train-*
    - split: validation
      path: data/validation-*
    - split: test
      path: data/test-*
  dataset_info:
    features:
    - name: id
      dtype: string
    - name: split
      dtype: string
    - name: rating
      dtype: int64
    - name: category
      dtype: string
    - name: day
      dtype: int64
    - name: month
      dtype: int64
    - name: year
      dtype: int64
    - name: excerpt
      dtype: string
    - name: language
      dtype: string
    - name: source
      dtype: string
    - name: authors
      dtype: string
    - name: title
      dtype: string
    - name: url
      dtype: string
    - name: text
      dtype: string
    splits:
    - name: train
      num_bytes: 94334212
      num_examples: 34749
    - name: validation
      num_bytes: 13597517
      num_examples: 4348
    - name: test
      num_bytes: 13787751
      num_examples: 4340
    download_size: 77286913
    dataset_size: 121719480
  homepage: https://github.com/ltgoslo/norec
  citation: |-
    @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}
    }
    }
task_categories:
- text-classification
---
# NoReC: The Norwegian Review Corpus
This is the official repository for the Norwegian Review Corpus (NoReC), created for the purpose of training and evaluating models for document-level sentiment analysis. 
More than 43,000 full-text reviews have been 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. Each review is labeled with a manually assigned score of 1–6, as provided by the rating of the original author. The accompanying [paper](http://www.lrec-conf.org/proceedings/lrec2018/pdf/851.pdf) by Velldal et al. at LREC 2018 describes the (initial release of the) data in more detail. 

## Dataset details
- **The columns are:**
`id, split, rating, category, day, month, year, excerpt, language, source, authors, title, url, text` where basic usage has `text` as the input and `rating` as the output.


- **Funding:** 
NoReC was created as part of the [SANT](https://www.mn.uio.no/ifi/english/research/projects/sant/) project (Sentiment Analysis for Norwegian Text),
a collaboration between the Language Technology Group (LTG) at the Department of Informatics at the University of Oslo, the Norwegian Broadcasting Corporation (NRK), Schibsted Media Group and Aller Media.


- **Language(s):** Norwegian Bokmål (nb) and Norwegian Nynorsk (nn)
- **License:** [See the license for the NoReC corpus](https://github.com/ltgoslo/norec), copied here for convenience:
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. 

- **Dataset Sources** 
This verion of the corpus comprises 43,436 review texts extracted from eight different news sources: Dagbladet, VG, Aftenposten, Bergens Tidende, Fædrelandsvennen, Stavanger Aftenblad, DinSide.no and P3.no.
<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/ltgoslo/norec
- **Paper :**  [The accompanying paper by Velldal et al. at LREC 2018](http://www.lrec-conf.org/proceedings/lrec2018/pdf/851.pdf) describes the (initial release of the) data in more detail.


## Uses

The dataset is intended for document-level sentiment analysis, to learn to predict the `rating` from the `text`. The field `category` can be considered the "domain" of each text. By filtering in and out `category` values, one may inspect cross-domain performance of a model. The other fields contain metadata preserved from the original version of the dataset, and may be used for further filtering and analyses.


## Source Data
This _2nd release, v.2.1_ of the corpus comprises 43,436 review texts extracted from eight different news sources: Dagbladet (db), VG (vg), Aftenposten (ap), Bergens Tidende (bt), Fædrelandsvennen (fvn), Stavanger Aftenblad (sa), DinSide.no (dinside) and P3.no (p3).  

In terms of publishing date the reviews mainly cover the time span 2003–2019, although it also includes a handful of reviews dating back as far as 1998.


# Some statistics
## Distribution over year and publication source
All splits combined

|   year |   ap |   bt |   db        |   dinside |   fvn |   p3 |   sa |   vg |   Total |
|-------:|-----:|-----:|------------:|----------:|------:|-----:|-----:|-----:|--------:|
|  2003* |    0 |    4 |           0 |       143 |     0 |   25 |    0 |  286 |     458 |
|   2004 |    0 |   44 |           0 |       142 |     0 |   12 |   19 |  984 |    1201 |
|   2005 |    0 |    0 |           0 |       179 |     0 |    6 |  224 |  909 |    1318 |
|   2006 |    0 |    0 |           0 |       240 |     0 |   11 |  294 |  778 |    1323 |
|   2007 |    0 |    0 |           0 |       139 |     0 |  127 |  400 |  725 |    1391 |
|   2008 |    0 |    0 |           0 |       119 |     0 |  216 |  369 |  739 |    1443 |
|   2009 |    0 |   52 |         377 |       163 |    27 |  428 |  259 |  815 |    2121 |
|   2010 |    0 |  100 |         642 |       260 |   156 |  571 |  309 |  769 |    2807 |
|   2011 |    1 |   51 |         592 |       284 |   146 |  652 |  362 |  900 |    2988 |
|   2012 |    2 |  150 |         613 |       257 |   332 |  611 |  561 |  763 |    3289 |
|   2013 |    4 |  160 |         527 |       216 |   213 |  619 |  433 | 1058 |    3230 |
|   2014 |   39 |  291 |         501 |       236 |   357 |  546 |  387 | 1191 |    3548 |
|   2015 |  249 |  235 |         728 |       245 |   456 |  499 |  620 |  849 |    3881 |
|   2016 |  309 |  340 |         809 |       177 |   321 |  439 |  682 |  715 |    3792 |
|   2017 |  649 |  491 |         921 |       248 |   692 |  567 |  822 |  687 |    5077 |
|   2018 |  605 |  470 |         885 |       194 |   466 |  339 |  860 |  492 |    4311 |
|   2019 |  260 |  167 |          95 |        30 |   160 |   36 |  346 |  165 |    1259 |

`2003*`: Including the 31 documents 1998-2002

## Distribution over split and rating 

| split   |   1 |    2 |    3 |     4 |     5 |    6 |   Total |
|:--------|----:|-----:|-----:|------:|------:|-----:|--------:|
| dev     |  51 |  225 |  707 |  1409 |  1678 |  278 |    4348 |
| test    |  27 |  242 |  706 |  1385 |  1714 |  266 |    4340 |
| train   | 379 | 2287 | 6004 | 11304 | 12614 | 2161 |   34749 |

## Distribution over split and category
| split   |   games |   literature |   misc |   music |   products |   restaurants |   screen |   sports |   stage |   Total |
|:--------|--------:|-------------:|-------:|--------:|-----------:|--------------:|---------:|---------:|--------:|--------:|
| dev     |     179 |          539 |     28 |    1445 |        347 |            94 |     1569 |       15 |     132 |    4348 |
| test    |     180 |          547 |     24 |    1444 |        345 |            98 |     1579 |       16 |     107 |    4340 |
| train   |    1453 |         4337 |    156 |   11777 |       2771 |           745 |    12536 |      118 |     856 |   34749 |




## Citation

```
@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

https://huggingface.co/ltg