Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
Polish
Multilinguality:
monolingual
Language Creators:
other
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
| annotations_creators: | |
| - expert-generated | |
| language_creators: | |
| - other | |
| language: | |
| - pl | |
| license: | |
| - cc-by-sa-4.0 | |
| multilinguality: | |
| - monolingual | |
| pretty_name: 'Polemo2' | |
| size_categories: | |
| - 8K | |
| - 1K<n<10K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - sentiment-classification | |
| # Polemo2 | |
| ## Description | |
| The PolEmo2.0 is a dataset of online consumer reviews from four domains: medicine, hotels, products, and university. It is human-annotated on a level of full reviews and individual sentences. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in the 2+1 scheme, which gives a total of 197,046 annotations. About 85% of the reviews are from the medicine and hotel domains. Each review is annotated with four labels: positive, negative, neutral, or ambiguous. | |
| ## Tasks (input, output and metrics) | |
| The task is to predict the correct label of the review. | |
| **Input** ('*text*' column): sentence | |
| **Output** ('*target*' column): label for sentence sentiment ('zero': neutral, 'minus': negative, 'plus': positive, 'amb': ambiguous) | |
| **Domain**: Online reviews | |
| **Measurements**: Accuracy, F1 Macro | |
| **Example**: | |
| Input: `Na samym wej艣ciu hotel 艣mierdzi . W pokojach jest ple艣艅 na 艣cianach , brudny dywan . W 艂azience 艣mierdzi chemi膮 , hotel nie grzeje w pokojach panuje ch艂贸d . Wyposa偶enie pokoju jest stare , kran si臋 rusza , drzwi na balkon nie domykaj膮 si臋 . Jedzenie jest w ma艂ych ilo艣ciach i nie smaczne . Nie polecam nikomu tego hotelu .` | |
| Input (translated by DeepL): `At the very entrance the hotel stinks . In the rooms there is mold on the walls , dirty carpet . The bathroom smells of chemicals , the hotel does not heat in the rooms are cold . The room furnishings are old , the faucet moves , the door to the balcony does not close . The food is in small quantities and not tasty . I would not recommend this hotel to anyone .` | |
| Output: `1` (negative) | |
| ## Data splits | |
| | Subset | Cardinality | | |
| |--------|------------:| | |
| | train | 6573 | | |
| | val | 823 | | |
| | test | 820 | | |
| ## Class distribution | |
| | Class | train | dev | test | | |
| |:--------|--------:|-------------:|-------:| | |
| | minus | 0.3756 | 0.3694 | 0.4134 | | |
| | plus | 0.2775 | 0.2868 | 0.2768 | | |
| | amb | 0.1991 | 0.1883 | 0.1659 | | |
| | zero | 0.1477 | 0.1555 | 0.1439 | | |
| ## Citation | |
| ``` | |
| @inproceedings{kocon-etal-2019-multi, | |
| title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews", | |
| author = "Koco{\'n}, Jan and | |
| Mi{\l}kowski, Piotr and | |
| Za{\'s}ko-Zieli{\'n}ska, Monika", | |
| booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)", | |
| month = nov, | |
| year = "2019", | |
| address = "Hong Kong, China", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://aclanthology.org/K19-1092", | |
| doi = "10.18653/v1/K19-1092", | |
| pages = "980--991", | |
| abstract = "In this article we present an extended version of PolEmo {--} a corpus of consumer reviews from 4 domains: medicine, hotels, products and school. Current version (PolEmo 2.0) contains 8,216 reviews having 57,466 sentences. Each text and sentence was manually annotated with sentiment in 2+1 scheme, which gives a total of 197,046 annotations. We obtained a high value of Positive Specific Agreement, which is 0.91 for texts and 0.88 for sentences. PolEmo 2.0 is publicly available under a Creative Commons copyright license. We explored recent deep learning approaches for the recognition of sentiment, such as Bi-directional Long Short-Term Memory (BiLSTM) and Bidirectional Encoder Representations from Transformers (BERT).", | |
| } | |
| ``` | |
| ## License | |
| ``` | |
| Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) | |
| ``` | |
| ## Links | |
| [HuggingFace](https://huggingface.co/datasets/clarin-pl/polemo2-official) | |
| [Source](https://clarin-pl.eu/dspace/handle/11321/710) | |
| [Paper](https://aclanthology.org/K19-1092/) | |
| ## Examples | |
| ### Loading | |
| ```python | |
| from pprint import pprint | |
| from datasets import load_dataset | |
| dataset = load_dataset("clarin-pl/polemo2-official") | |
| pprint(dataset['train'][0]) | |
| # {'target': 1, | |
| # 'text': 'Na samym wej艣ciu hotel 艣mierdzi . W pokojach jest ple艣艅 na 艣cianach ' | |
| # ', brudny dywan . W 艂azience 艣mierdzi chemi膮 , hotel nie grzeje w ' | |
| # 'pokojach panuje ch艂贸d . Wyposa偶enie pokoju jest stare , kran si臋 ' | |
| # 'rusza , drzwi na balkon nie domykaj膮 si臋 . Jedzenie jest w ma艂ych ' | |
| # 'ilo艣ciach i nie smaczne . Nie polecam nikomu tego hotelu .'} | |
| ``` | |
| ### Evaluation | |
| ```python | |
| import random | |
| from pprint import pprint | |
| from datasets import load_dataset, load_metric | |
| dataset = load_dataset("clarin-pl/polemo2-official") | |
| references = dataset["test"]["target"] | |
| # generate random predictions | |
| predictions = [random.randrange(max(references) + 1) for _ in range(len(references))] | |
| acc = load_metric("accuracy") | |
| f1 = load_metric("f1") | |
| acc_score = acc.compute(predictions=predictions, references=references) | |
| f1_score = f1.compute(predictions=predictions, references=references, average='macro') | |
| pprint(acc_score) | |
| pprint(f1_score) | |
| # {'accuracy': 0.2475609756097561} | |
| # {'f1': 0.23747048177471738} | |
| ``` | |