metadata

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

Source

Paper

Examples

Loading

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

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}