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README.md

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----
-annotations_creators:
-- crowdsourced
-language:
-- sl
-language_creators:
-- found
-license:
-- cc-by-sa-4.0
-multilinguality:
-- monolingual
-pretty_name: SentiNews
-size_categories: []
-source_datasets:
-- original
-tags:
-- slovenian sentiment
-- news articles
-task_categories:
-- text-classification
-task_ids:
-- sentiment-classification
----
-
-# Dataset Card for SentiNews
-
-## Dataset Description
-
-- **Homepage:** https://github.com/19Joey85/Sentiment-annotated-news-corpus-and-sentiment-lexicon-in-Slovene  
-- **Paper:** Bučar, J., Žnidaršič, M. & Povh, J. Annotated news corpora and a lexicon for sentiment analysis in Slovene. Lang Resources & Evaluation 52, 895–919 (2018). https://doi.org/10.1007/s10579-018-9413-3
-
-### Dataset Summary
-
-SentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their sentiment by between two and six annotators.
-It is annotated at three granularities: 
-- document-level (config `document_level`, 10 427 documents), 
-- paragraph-level (config `paragraph_level`, 89 999 paragraphs), and 
-- sentence-level (config `sentence_level`, 168 899 sentences).
-
-### Supported Tasks and Leaderboards
-
-Sentiment classification, three classes (negative, neutral, positive).
-
-### Languages
-
-Slovenian.
-
-## Dataset Structure
-
-### Data Instances
-
-A sample instance from the sentence-level config:
-```
-{
-  'nid': 2, 
-  'content': 'Vilo Prešeren je na dražbi ministrstva za obrambo kupilo nepremičninsko podjetje Condor Real s sedežem v Lescah.', 
-  'sentiment': 'neutral', 
-  'pid': 1, 
-  'sid': 1
-}
-```
-
-### Data Fields
-
-The data fields are similar among all three configs, with the only difference being the IDs.
-
-- `nid`: a uint16 containing a unique ID of the news article (document). 
-- `content`: a string containing the body of the news article 
-- `sentiment`: the sentiment of the instance
-- `pid`: a uint8 containing the consecutive number of the paragraph inside the current news article, **not unique** (present in the configs `paragraph_level` and `sentence_level`)
-- `sid`: a uint8 containing the consecutive number of the sentence inside the current paragraph, **not unique** (present in the config `sentence_level`)
-
-## Additional Information
-
-### Dataset Curators
-
-Jože Bučar, Martin Žnidaršič, Janez Povh.
-
-### Licensing Information
-
-CC BY-SA 4.0
-
-### Citation Information
-
-```
-@article{buvcar2018annotated, 
-  title={Annotated news corpora and a lexicon for sentiment analysis in Slovene}, 
-  author={Bu{\v{c}}ar, Jo{\v{z}}e and {\v{Z}}nidar{\v{s}}i{\v{c}}, Martin and Povh, Janez}, 
-  journal={Language Resources and Evaluation}, 
-  volume={52}, 
-  number={3}, 
-  pages={895--919}, 
-  year={2018}, 
-  publisher={Springer}
-}
-```
-
-### Contributions
-
-Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.

dataset_infos.json

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-{"document_level": {"description": "SentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their \nsentiment by between 2 and 6 annotators. The news articles contain political, business, economic and financial content \nfrom the Slovenian news portals 24ur, Dnevnik, Finance, Rtvslo, and \u017durnal24. The texts were annotated using the \nfive-level Lickert scale (1 \u2013 very negative, 2 \u2013 negative, 3 \u2013 neutral, 4 \u2013 positive, and 5 \u2013 very positive) on three \nlevels of granularity, i.e. on the document, paragraph, and sentence level. The final sentiment is determined using \nthe following criterion: negative (if average of scores \u2264 2.4); neutral (if average of scores is between 2.4 and 3.6); \npositive (average of annotated scores \u2265 3.6).\n", "citation": "@article{buvcar2018annotated, \n        title={Annotated news corpora and a lexicon for sentiment analysis in Slovene}, \n        author={Bu{\u000b{c}}ar, Jo{\u000b{z}}e and {\u000b{Z}}nidar{\u000b{s}}i{\u000b{c}}, Martin and Povh, Janez}, \n        journal={Language Resources and Evaluation}, \n        volume={52}, \n        number={3}, \n        pages={895--919}, \n        year={2018}, \n        publisher={Springer}\n}\n", "homepage": "https://github.com/19Joey85/Sentiment-annotated-news-corpus-and-sentiment-lexicon-in-Slovene/", "license": "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"nid": {"dtype": "uint16", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}, "sentiment": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "content", "output": "sentiment"}, "task_templates": null, "builder_name": "sentinews", "config_name": "document_level", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 21726849, "num_examples": 10427, "dataset_name": "sentinews"}}, "download_checksums": {"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1110/SentiNews_document-level.txt": {"num_bytes": 23755522, "checksum": "060fe71d719d79e3bcba335abfc146485fa2f3568b5cc1245bd694834d77190c"}}, "download_size": 23755522, "post_processing_size": null, "dataset_size": 21726849, "size_in_bytes": 45482371}, "paragraph_level": {"description": "SentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their \nsentiment by between 2 and 6 annotators. The news articles contain political, business, economic and financial content \nfrom the Slovenian news portals 24ur, Dnevnik, Finance, Rtvslo, and \u017durnal24. The texts were annotated using the \nfive-level Lickert scale (1 \u2013 very negative, 2 \u2013 negative, 3 \u2013 neutral, 4 \u2013 positive, and 5 \u2013 very positive) on three \nlevels of granularity, i.e. on the document, paragraph, and sentence level. The final sentiment is determined using \nthe following criterion: negative (if average of scores \u2264 2.4); neutral (if average of scores is between 2.4 and 3.6); \npositive (average of annotated scores \u2265 3.6).\n", "citation": "@article{buvcar2018annotated, \n        title={Annotated news corpora and a lexicon for sentiment analysis in Slovene}, \n        author={Bu{\u000b{c}}ar, Jo{\u000b{z}}e and {\u000b{Z}}nidar{\u000b{s}}i{\u000b{c}}, Martin and Povh, Janez}, \n        journal={Language Resources and Evaluation}, \n        volume={52}, \n        number={3}, \n        pages={895--919}, \n        year={2018}, \n        publisher={Springer}\n}\n", "homepage": "https://github.com/19Joey85/Sentiment-annotated-news-corpus-and-sentiment-lexicon-in-Slovene/", "license": "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"nid": {"dtype": "uint16", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}, "sentiment": {"dtype": "string", "id": null, "_type": "Value"}, "pid": {"dtype": "uint8", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "content", "output": "sentiment"}, "task_templates": null, "builder_name": "sentinews", "config_name": "paragraph_level", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 23107429, "num_examples": 89999, "dataset_name": "sentinews"}}, "download_checksums": {"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1110/SentiNews_paragraph-level.txt": {"num_bytes": 24342387, "checksum": "c45535325db5a6ef5073d7a594c68e5a5c60983d2e1b277f0dbf76f9975a9f18"}}, "download_size": 24342387, "post_processing_size": null, "dataset_size": 23107429, "size_in_bytes": 47449816}, "sentence_level": {"description": "SentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their \nsentiment by between 2 and 6 annotators. The news articles contain political, business, economic and financial content \nfrom the Slovenian news portals 24ur, Dnevnik, Finance, Rtvslo, and \u017durnal24. The texts were annotated using the \nfive-level Lickert scale (1 \u2013 very negative, 2 \u2013 negative, 3 \u2013 neutral, 4 \u2013 positive, and 5 \u2013 very positive) on three \nlevels of granularity, i.e. on the document, paragraph, and sentence level. The final sentiment is determined using \nthe following criterion: negative (if average of scores \u2264 2.4); neutral (if average of scores is between 2.4 and 3.6); \npositive (average of annotated scores \u2265 3.6).\n", "citation": "@article{buvcar2018annotated, \n        title={Annotated news corpora and a lexicon for sentiment analysis in Slovene}, \n        author={Bu{\u000b{c}}ar, Jo{\u000b{z}}e and {\u000b{Z}}nidar{\u000b{s}}i{\u000b{c}}, Martin and Povh, Janez}, \n        journal={Language Resources and Evaluation}, \n        volume={52}, \n        number={3}, \n        pages={895--919}, \n        year={2018}, \n        publisher={Springer}\n}\n", "homepage": "https://github.com/19Joey85/Sentiment-annotated-news-corpus-and-sentiment-lexicon-in-Slovene/", "license": "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"nid": {"dtype": "uint16", "id": null, "_type": "Value"}, "content": {"dtype": "string", "id": null, "_type": "Value"}, "sentiment": {"dtype": "string", "id": null, "_type": "Value"}, "pid": {"dtype": "uint8", "id": null, "_type": "Value"}, "sid": {"dtype": "uint8", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "content", "output": "sentiment"}, "task_templates": null, "builder_name": "sentinews", "config_name": "sentence_level", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 24641935, "num_examples": 168899, "dataset_name": "sentinews"}}, "download_checksums": {"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1110/SentiNews_sentence-level.txt": {"num_bytes": 27220223, "checksum": "4382e064543b7fc6d61cc76bc2f16f8f37c80065dbc4f87b888919d1f45bb9c1"}}, "download_size": 27220223, "post_processing_size": null, "dataset_size": 24641935, "size_in_bytes": 51862158}}

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sentinews.py

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-"""SentiNews: Manually sentiment annotated Slovenian news corpus."""
-
-
-import csv
-
-import datasets
-
-_CITATION = """\
-@article{buvcar2018annotated, 
-        title={Annotated news corpora and a lexicon for sentiment analysis in Slovene}, 
-        author={Bu{\v{c}}ar, Jo{\v{z}}e and {\v{Z}}nidar{\v{s}}i{\v{c}}, Martin and Povh, Janez}, 
-        journal={Language Resources and Evaluation}, 
-        volume={52}, 
-        number={3}, 
-        pages={895--919}, 
-        year={2018}, 
-        publisher={Springer}
-}
-"""
-
-_DESCRIPTION = """\
-SentiNews is a Slovenian sentiment classification dataset, consisting of news articles manually annotated with their 
-sentiment by between 2 and 6 annotators. The news articles contain political, business, economic and financial content 
-from the Slovenian news portals 24ur, Dnevnik, Finance, Rtvslo, and Žurnal24. The texts were annotated using the 
-five-level Lickert scale (1 – very negative, 2 – negative, 3 – neutral, 4 – positive, and 5 – very positive) on three 
-levels of granularity, i.e. on the document, paragraph, and sentence level. The final sentiment is determined using 
-the following criterion: negative (if average of scores ≤ 2.4); neutral (if average of scores is between 2.4 and 3.6); 
-positive (average of annotated scores ≥ 3.6).
-"""
-
-_HOMEPAGE = "https://github.com/19Joey85/Sentiment-annotated-news-corpus-and-sentiment-lexicon-in-Slovene/"
-
-_LICENSE = "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"
-
-_URLS = {
-    "document_level": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1110/SentiNews_document-level.txt",
-    "paragraph_level": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1110/SentiNews_paragraph-level.txt",
-    "sentence_level": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1110/SentiNews_sentence-level.txt"
-}
-
-
-class Sentinews(datasets.GeneratorBasedBuilder):
-    """SentiNews: Manually sentiment annotated Slovenian news corpus. Version 1.0."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="document_level", version=VERSION, description="Dataset annotated at document level."),
-        datasets.BuilderConfig(name="paragraph_level", version=VERSION, description="Dataset annotated at paragraph level."),
-        datasets.BuilderConfig(name="sentence_level", version=VERSION, description="Dataset annotated at sentence level."),
-    ]
-
-    def _info(self):
-        _config_features = {
-            "nid": datasets.Value("uint16"),
-            "content": datasets.Value("string"),
-            "sentiment": datasets.Value("string")
-        }
-
-        if self.config.name == "paragraph_level":
-            _config_features["pid"] = datasets.Value("uint8")
-        elif self.config.name == "sentence_level":
-            _config_features["pid"] = datasets.Value("uint8")
-            _config_features["sid"] = datasets.Value("uint8")
-
-        features = datasets.Features(_config_features)
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=("content", "sentiment"),
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        urls = _URLS[self.config.name]
-        data_file = dl_manager.download_and_extract(urls)
-        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"data_file": data_file})]
-
-    def _generate_examples(self, data_file):
-        _keys_to_return = ["nid", "content", "sentiment"]
-
-        if self.config.name == "paragraph_level":
-            _keys_to_return.append("pid")
-        elif self.config.name == "sentence_level":
-            _keys_to_return.append("pid")
-            _keys_to_return.append("sid")
-
-        with open(data_file, encoding="utf-8") as f:
-            data = csv.DictReader(f, delimiter="\t")
-            for idx, row in enumerate(data):
-                yield idx, {_k: row[_k] for _k in _keys_to_return}