Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
sentiment-classification
Languages:
Polish
Size:
1K - 10K
License:
| annotations_creators: | |
| - expert-generated | |
| language_creators: | |
| - other | |
| language: | |
| - pl | |
| license: | |
| - mit | |
| multilinguality: | |
| - monolingual | |
| pretty_name: 'AspectEmo' | |
| size_categories: | |
| - 1K | |
| - 1K<n<10K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - token-classification | |
| task_ids: | |
| - sentiment-classification | |
| # AspectEmo | |
| ## Description | |
| AspectEmo Corpus is an extended version of a publicly available PolEmo 2.0 corpus of Polish customer reviews used in many projects on the use of different methods in sentiment analysis. The AspectEmo corpus consists of four subcorpora, each containing online customer reviews from the following domains: school, medicine, hotels, and products. All documents are annotated at the aspect level with six sentiment categories: strong negative (minus_m), weak negative (minus_s), neutral (zero), weak positive (plus_s), strong positive (plus_m). | |
| ## Versions | |
| | version | config name | description | default | notes | | |
| |---------|-------------|--------------------------------|---------|------------------| | |
| | 1.0 | "1.0" | The version used in the paper. | YES | | | |
| | 2.0 | - | Some bugs fixed. | NO | work in progress | | |
| ## Tasks (input, output and metrics) | |
| Aspect-based sentiment analysis (ABSA) is a text analysis method that categorizes data by aspects and identifies the sentiment assigned to each aspect. It is the sequence tagging task. | |
| **Input** ('*tokens'* column): sequence of tokens | |
| **Output** ('*labels'* column): sequence of predicted tokens’ classes ("O" + 6 possible classes: strong negative (a_minus_m), weak negative (a_minus_s), neutral (a_zero), weak positive (a_plus_s), strong positive (a_plus_m), ambiguous (a_amb) ) | |
| **Domain**: school, medicine, hotels and products | |
| **Measurements**: F1-score (seqeval) | |
| **Example***:* | |
| Input: `['Dużo', 'wymaga', ',', 'ale', 'bardzo', 'uczciwy', 'i', 'przyjazny', 'studentom', '.', 'Warto', 'chodzić', 'na', 'konsultacje', '.', 'Docenia', 'postępy', 'i', 'zaangażowanie', '.', 'Polecam', '.']` | |
| Input (translated by DeepL): `'Demands a lot , but very honest and student friendly . Worth going to consultations . Appreciates progress and commitment . I recommend .'` | |
| Output: `['O', 'a_plus_s', 'O', 'O', 'O', 'a_plus_m', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'a_zero', 'O', 'a_plus_m', 'O', 'O', 'O', 'O', 'O', 'O']` | |
| ## Data splits | |
| | Subset | Cardinality (sentences) | | |
| |:-------|------------------------:| | |
| | train | 1173 | | |
| | val | 0 | | |
| | test | 292 | | |
| ## Class distribution(without "O") | |
| | Class | train | validation | test | | |
| |:----------|--------:|-------------:|-------:| | |
| | a_plus_m | 0.359 | - | 0.369 | | |
| | a_minus_m | 0.305 | - | 0.377 | | |
| | a_zero | 0.234 | - | 0.182 | | |
| | a_minus_s | 0.037 | - | 0.024 | | |
| | a_plus_s | 0.037 | - | 0.015 | | |
| | a_amb | 0.027 | - | 0.033 | | |
| ## Citation | |
| ``` | |
| @misc{11321/849, | |
| title = {{AspectEmo} 1.0: Multi-Domain Corpus of Consumer Reviews for Aspect-Based Sentiment Analysis}, | |
| author = {Koco{\'n}, Jan and Radom, Jarema and Kaczmarz-Wawryk, Ewa and Wabnic, Kamil and Zaj{\c a}czkowska, Ada and Za{\'s}ko-Zieli{\'n}ska, Monika}, | |
| url = {http://hdl.handle.net/11321/849}, | |
| note = {{CLARIN}-{PL} digital repository}, | |
| copyright = {The {MIT} License}, | |
| year = {2021} | |
| } | |
| ``` | |
| ## License | |
| ``` | |
| The MIT License | |
| ``` | |
| ## Links | |
| [HuggingFace](https://huggingface.co/datasets/clarin-pl/aspectemo) | |
| [Source](https://clarin-pl.eu/dspace/handle/11321/849) | |
| [Paper](https://sentic.net/sentire2021kocon.pdf) | |
| ## Examples | |
| ### Loading | |
| ```python | |
| from pprint import pprint | |
| from datasets import load_dataset | |
| dataset = load_dataset("clarin-pl/aspectemo") | |
| pprint(dataset['train'][20]) | |
| # {'labels': [0, 4, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0, 0], | |
| # 'tokens': ['Dużo', | |
| # 'wymaga', | |
| # ',', | |
| # 'ale', | |
| # 'bardzo', | |
| # 'uczciwy', | |
| # 'i', | |
| # 'przyjazny', | |
| # 'studentom', | |
| # '.', | |
| # 'Warto', | |
| # 'chodzić', | |
| # 'na', | |
| # 'konsultacje', | |
| # '.', | |
| # 'Docenia', | |
| # 'postępy', | |
| # 'i', | |
| # 'zaangażowanie', | |
| # '.', | |
| # 'Polecam', | |
| # '.']} | |
| ``` | |
| ### Evaluation | |
| ```python | |
| import random | |
| from pprint import pprint | |
| from datasets import load_dataset, load_metric | |
| dataset = load_dataset("clarin-pl/aspectemo") | |
| references = dataset["test"]["labels"] | |
| # generate random predictions | |
| predictions = [ | |
| [ | |
| random.randrange(dataset["train"].features["labels"].feature.num_classes) | |
| for _ in range(len(labels)) | |
| ] | |
| for labels in references | |
| ] | |
| # transform to original names of labels | |
| references_named = [ | |
| [dataset["train"].features["labels"].feature.names[label] for label in labels] | |
| for labels in references | |
| ] | |
| predictions_named = [ | |
| [dataset["train"].features["labels"].feature.names[label] for label in labels] | |
| for labels in predictions | |
| ] | |
| # transform to BILOU scheme | |
| references_named = [ | |
| [f"U-{label}" if label != "O" else label for label in labels] | |
| for labels in references_named | |
| ] | |
| predictions_named = [ | |
| [f"U-{label}" if label != "O" else label for label in labels] | |
| for labels in predictions_named | |
| ] | |
| # utilise seqeval to evaluate | |
| seqeval = load_metric("seqeval") | |
| seqeval_score = seqeval.compute( | |
| predictions=predictions_named, | |
| references=references_named, | |
| scheme="BILOU", | |
| mode="strict", | |
| ) | |
| pprint(seqeval_score) | |
| # {'a_amb': {'f1': 0.00597237775289287, | |
| # 'number': 91, | |
| # 'precision': 0.003037782418834251, | |
| # 'recall': 0.17582417582417584}, | |
| # 'a_minus_m': {'f1': 0.048306148055207034, | |
| # 'number': 1039, | |
| # 'precision': 0.0288551620760727, | |
| # 'recall': 0.1482194417709336}, | |
| # 'a_minus_s': {'f1': 0.004682997118155619, | |
| # 'number': 67, | |
| # 'precision': 0.0023701002734731083, | |
| # 'recall': 0.19402985074626866}, | |
| # 'a_plus_m': {'f1': 0.045933014354066985, | |
| # 'number': 1015, | |
| # 'precision': 0.027402473834443386, | |
| # 'recall': 0.14187192118226602}, | |
| # 'a_plus_s': {'f1': 0.0021750951604132683, | |
| # 'number': 41, | |
| # 'precision': 0.001095690284879474, | |
| # 'recall': 0.14634146341463414}, | |
| # 'a_zero': {'f1': 0.025159400310184387, | |
| # 'number': 501, | |
| # 'precision': 0.013768389287061486, | |
| # 'recall': 0.14570858283433133}, | |
| # 'overall_accuracy': 0.13970115681233933, | |
| # 'overall_f1': 0.02328248652368391, | |
| # 'overall_precision': 0.012639312620633834, | |
| # 'overall_recall': 0.14742193173565724} | |
| ``` |