Datasets:
license: mit
task_categories:
- text-classification
language:
- en
tags:
- emotion
- affect
pretty_name: OME
size_categories:
- 10K<n<100K
Orthogonal Model of Emotions
Abbreviated: OME
The fifth version of the OME dataset has 26 categories for classifying emotion in English language examples in a curated dataset deriving emotional clusters using dimensions of Subjectivity, Relativity, and Generativity. Additional dimensions of Clarity, now simpler with three levels, and Compassion, using rate of change to linearize the data, were used to map seven population clusters of ontological experiences categorized as Trust or Love, Happiness or Pleasure, Jealousy or Envy, Shame or Guilt, Anger or Disgust, Fear or Anxiety, and Sadness or Trauma. Edge cases, neutrality, and simple sentiments, such as positive and negative, are also used as null cases in classification theorized by OME.
Author
C.J. Pitchford
Creation Date
Originally created 2016, first version published September, 2017, at Medium.
Version
v5
Categories
[Clusters listed in brackets (alphabetically) organize the dataset, but aren't labels]
- [Anger or Disgust]
- anger-and-disgust-clear
- anger-and-disgust-conspicuous
- anger-and-disgust-presumed
- [Fear or Anxiety]
- fear-and-anxiety-clear
- fear-and-anxiety-conspicuous
- fear-and-anxiety-presumed
- [Guilt or Shame]
- guilt-and-shame-clear
- guilt-and-shame-conspicuous
- guilt-and-shame-presumed
- [Happiness or Pleasure]
- happiness-and-pleasure-clear
- happiness-and-pleasure-conspicuous
- happiness-and-pleasure-presumed
- [Jealousy or Envy]
- jealousy-and-envy-clear
- jealousy-and-envy-conspicuous
- jealousy-and-envy-presumed
- [Neutral or Edge Cases]
- negative-conspicuous
- negative-presumed
- neutral-presumed
- positive-conspicuous
- positive-presumed
- [Sadness or Trauma]
- sadness-and-trauma-clear
- sadness-and-trauma-conspicuous
- sadness-and-trauma-presumed
- [Trust or Love]
- trust-and-love-clear
- trust-and-love-conspicuous
- trust-and-love-presumed
Baseline Model
The baeline model was created using Transformers and PyTorch and an earlier version of this dataset and theoretical foundation.