Dataset Card for NoReC_sentence

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 fine-grained annotations of 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. The data comes in three different versions: binary (only positive or negative sentences), ternary (additionally includes neutral examples), and mixed (additionaly includes sentences with mixed polarity).

• Curated by: The SANT project (Sentiment Analysis for Norwegian Text) at the Language Technology Group (LTG) at the University of Oslo
• Funded by: The SANT is funded by the Research Council of Norway (NFR grant number 270908).
• Shared by: The SANT project (Sentiment Analysis for Norwegian Text) at the Language Technology Group (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 (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

• Repository: https://github.com/ltgoslo/norec_sentence
• Paper: The underlying NoReC_fine dataset is described in the paper A Fine-Grained Sentiment Dataset for Norwegian 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 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

The data comes in three different versions:

• Binary: only includes examples with positive or negative polarity
• Ternary: additionally includes neutral examples
• Mixed: additionaly includes examples with mixed (i.e. both positive and negative) polarity

Each example in the data comes with three fields: a uniqe identifier ('id'), the sentence itself ('review'), and the polarity label ('sentiment'). Depending on the dataset version used, the label can be 0 (negative), 1 (positive), or 2 (neutral). For the mixed version the sentiment label is a list, reflecting the fact that a sentence can be both positive or negative. See examples below for each the three dataset versions.

Binary:

{'id': '000335-02-01', 'review': 'Til tider ufrivillig morsomt .', 'sentiment': 0}
{'id': '704010-01-01', 'review': 'Ekstremt vitalt', 'sentiment': 1}

Ternary:

{'id': '000335-02-01', 'review': 'Til tider ufrivillig morsomt .', 'sentiment': 0}
{'id': '704010-01-01', 'review': 'Ekstremt vitalt', 'sentiment': 1}
{'id': '000778-57-02', 'review': 'Del dine synspunkter i kommentarfeltet .', 'sentiment': 2}

Mixed:

{'id': '000335-02-01', 'review': 'Til tider ufrivillig morsomt .', 'sentiment': [0]}
{'id': '704010-01-01', 'review': 'Ekstremt vitalt', 'sentiment': [1]}
{'id': '000778-57-02', 'review': 'Del dine synspunkter i kommentarfeltet .', 'sentiment': [2]}
{'id': '004702-02-01', 'review': 'Forviklingskomedie med brodd og bismak .', 'sentiment': [0, 1]}

Dataset Creation

Curation Rationale

The aggregated 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 (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

The data does not contain information considered personal or sensitive.

Recommendations

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

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

[email protected] and [email protected]