Datasets:
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:
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num_examples: 3894
- name: validation
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- 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:
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num_examples: 7973
- name: validation
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- name: test
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num_examples: 1181
download_size: 740756
dataset_size: 1132724
- config_name: mixed
features:
- name: id
dtype: string
- name: review
dtype: string
- name: sentiment
sequence: int64
splits:
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features:
- name: id
dtype: string
- name: review
dtype: string
- name: sentiment
dtype: int64
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num_examples: 7973
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num_examples: 1181
download_size: 745057
dataset_size: 1220574
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-*
- config_name: mixed
data_files:
- split: train
path: mixed/train-*
- split: validation
path: mixed/validation-*
- split: test
path: mixed/test-*
- config_name: ternary
data_files:
- split: train
path: ternary/train-*
- split: validation
path: ternary/validation-*
- split: test
path: ternary/test-*
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 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. 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 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
[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 (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