Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
multi-label-classification
Languages:
Polish
Size:
10K - 100K
License:
File size: 4,967 Bytes
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---
dataset_info:
features:
- name: text
dtype: string
- name: Joy
dtype: bool
- name: Trust
dtype: bool
- name: Anticipation
dtype: bool
- name: Surprise
dtype: bool
- name: Fear
dtype: bool
- name: Sadness
dtype: bool
- name: Disgust
dtype: bool
- name: Anger
dtype: bool
- name: Positive
dtype: bool
- name: Negative
dtype: bool
- name: Neutral
dtype: bool
splits:
- name: train
num_bytes: 869804
num_examples: 7169
- name: val
num_bytes: 160871
num_examples: 1401
- name: test
num_bytes: 176693
num_examples: 1431
download_size: 840179
dataset_size: 1207368
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
task_categories:
- text-classification
language:
- pl
tags:
- emotion
- sentence-classification
- emotion recognition
task_ids:
- multi-label-classification
license: cc-by-4.0
---
## Dataset
The dataset is made up of consumer reviews written in Polish. Those reviews belong to four domains: hotels, medicine, products, and university. This collection also contains non-opinion informative texts belonging to the same domains (meaning they are mostly neutral). Each sentence, as well as all the reviews as a whole, are annotated with emotions from the Plutchnik's wheel of emotions (joy, trust, anticipation, surprise, fear, sadness, disgust, anger), as well as the perceived sentiment (positive, negative, neutral), with ambivalent sentiment being labeled using both positive and negative labels. The dataset was annotated by six people who did not see each other's decisions. These annotations were aggregated by selecting labels annotated by at least 2 out of 6 people, meaning controversial texts and sentences can be annotated with opposing emotions. While each sentence has its own annotation, they were created in the context of the whole review.
For more information about this dataset, see references [1](#ref-1) and [2](#ref-2).
### Training set
Training data consists of 776 reviews containing 6393 sentences randomly selected from the whole dataset. The split was done on the level of whole reviews, meaning no reviews are split between sets.
### Test sets
Two test sets contain 167 reviews, each containing 1234 and 1264 sentence annotations.
### Dataset format
The datasets are stored in three directories (training and two test sets). All datasets have the same format.
Input rows contain ordered sentences of reviews. Each review ends with a sentence made out of only the symbol #. This sentence annotation corresponds to the annotation of the whole review and is not a sentence annotation. This sentence is not a part of the original review and should not be treated as such. It only marks the end of the current review and the row that contains the corresponding review annotation. The next row after such a sentence corresponds to the first sentence of a different review.
Example:
This fragment of the training input file:
```
Była to pierwsza wizyta ale moze i ostatnia.
Lakarz troche apatyczny, nie wypowiadajacy sie jasno.
Mam zrobic jakies badanie ale nie dardzo wiem jakie.
Nie napisal skierowania/zalecenia, chyba mowil o gastrologii.
Powinnam byla byc bardzej wymagajaca i dopytujaca.
Nie polecam tego lekarza.
###########################
```
corresponds to annotations:
```
False False True False False True False False False True False
False False False False False True True False False True False
False False False True False True False False False True False
False False False True False True False False False True False
False False False True False True False True False True False
False False False False False True False False False True False
False False False True False True False False False True False
```
meaning sentences are labeled as:
```
"Była to pierwsza wizyta ale moze i ostatnia." - anticipation, sadness, negative
"Lakarz troche apatyczny, nie wypowiadajacy sie jasno." - sadness, disgust, negative
"Mam zrobic jakies badanie ale nie dardzo wiem jakie." - surprise, sadness, negative
"Nie napisal skierowania/zalecenia, chyba mowil o gastrologii." - surprise, sadness, negative
"Powinnam byla byc bardzej wymagajaca i dopytujaca." - surprise, sadness, anger, negative
"Nie polecam tego lekarza." - sadness, negative
```
and the review as a whole, starting from "Była to pierwsza wizyta ale moze i ostatnia." and ending at "Nie polecam tego lekarza." is labeled as: surprise, sadness, negative.
## References
<span id="ref-1">1. Koptyra, Bartłomiej, et al. "CLARIN-Emo: Training Emotion Recognition Models Using Human Annotation and ChatGPT." International Conference on Computational Science. Cham: Springer Nature Switzerland, 2023.</span>
<span id="ref-2">2. Kocoń, Jan, et al. "ChatGPT: Jack of all trades, master of none." Information Fusion (2023): 101861.</span> |