There is a multitude of ways to model emotions, and some of them are collected in the EmotionML vacabularies.
Really popular with engineers and non-psychologists are two approaches:
The emotion cube maps the emotional categories to a three dimensional space:
To do data augmentation, you can use the augment interface.
In the DATA section of your configuration file, you specify the name of the output list of files like so
[DATA]
augment = my_augmentations.csvand then call the interface:
python - nkululeko.augment --config myconfig.iniOnce you augmentations have been processed, you can add them to the training in a new experiment:
[DATA]
databases = ['original data', 'augment']
augment = my_augmentations.csv
augment.type = csv
augment.absolute_path = False
augment.split_strategy = trainSupervised vs. unsupervised
means the distinction whether your training data is annotated (or labeled) with respect to your task. An example: If you want to build a machine learner for human age estimation based on speech, you might give an algorithm a lot of examples of human speech annotated with the age of the person. This would be your training data and the approach would be supervised (by the age annotations). With unsupervised learning, you would give an algorithm simply a lot of human speech data and might ask it to cluster the data, based on differences. And might hope that the resulting clusters coincide with age.
Nkululeko exercise
-> Nkululeko: install the Berlin Emodb
This database contains examples of labels: