---
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).