|
--- |
|
license: mit |
|
task_categories: |
|
- text-classification |
|
language: |
|
- en |
|
tags: |
|
- emotion |
|
- complexity |
|
- readability |
|
- sentiment |
|
pretty_name: CAMEO |
|
size_categories: |
|
- 10K<n<100K |
|
--- |
|
# Dataset Card for CAMEO |
|
|
|
<!-- Provide a quick summary of the dataset. --> |
|
|
|
Dataset to accompany the EMNLP'23 paper titled: "Misery Loves Complexity: Exploring Linguistic Complexity in the Context of Emotion Detection". |
|
|
|
## Dataset Details |
|
|
|
50,000 subset from the GoEmotions Dataset automatically annotated with the following linguistic complexity measures: |
|
|
|
- idt: Incomplete Dependency Theory |
|
- dlt: Dependency Locality Theory |
|
- nnd: Nested-Nouns Distance |
|
- le: Left-embededness |
|
- percentage_polysyllable_words: % of polysyllable words |
|
- avg_conn_doc: Average connectives per sentence |
|
- number_of_uniq_entities: Number of unique named entities |
|
- average_word_len: Average word length |
|
- dale_word_frequency_score: DALE Word Frequency Score |
|
- avgtfidf: Average TF-IDF of all words based on the background corpus |
|
- avgll: Average Log-likelihood of all words based on the background corpus |
|
- type_token_ratio_perc: % Type-token ratio |
|
|
|
Please refer to the paper for further details on the metrics or other information. |
|
|
|
For details on how the data was collected or annotated for emotions. Please refer to the original [GoEmotions dataset](https://github.com/google-research/google-research/tree/master/goemotions). |
|
|
