language:
- it
license: cc-by-nc-sa-4.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: test_ood
path: data/test_ood-*
dataset_info:
features:
- name: id
dtype: string
- name: text
dtype: string
- name: emotion_labels
sequence:
class_label:
names:
'0': Anger
'1': Anticipation
'2': Disgust
'3': Fear
'4': Joy
'5': Love
'6': Neutral
'7': Sadness
'8': Surprise
'9': Trust
- name: target_labels
sequence:
class_label:
names:
'0': Direction
'1': Topic
splits:
- name: train
num_bytes: 1010988
num_examples: 5966
- name: test
num_bytes: 169792
num_examples: 1000
- name: test_ood
num_bytes: 137719
num_examples: 1000
download_size: 844581
dataset_size: 1318499
EMit
Disclaimer: This dataset is not the official EMit repository from EVALITA. For the official repository and more information, please visit the EVALITA EMit page or the EMit repository.
Overview
The EMit dataset is a comprehensive resource for the detection of emotions in Italian social media texts. This dataset was created for the EMit shared task, organized as part of Evalita 2023. The EMit dataset consists of social media messages about TV shows, TV series, music videos, and advertisements. Each message is annotated with one or more of the 8 primary emotions defined by Plutchik (anger, anticipation, disgust, fear, joy, sadness, surprise, trust), as well as an additional label “love.”
Annotations
The dataset includes multilabel annotations for each text, indicating the presence of specific emotions. An additional auxiliary subtask involves identifying the target of the affective comments, whether they are directed at the topic of the content or at issues under the control of the direction (e.g., production quality or artistic direction).
Structure
The dataset is composed of the following fields:
id: Identifier for the entry.text: Social media messages related to TV shows, TV series, music videos, and advertisements.emotion_labels: Anger, anticipation, disgust, fear, joy, sadness, surprise, trust, and love.target_labels: Topic, direction, both, or neither.