Intensional Learning to Efficiently Build up Automatically Annotated Emotion Corpora

Abstract

Textual emotion detection has a high impact on business, society, politics or education with applications such as, detecting depression or personality traits, suicide prevention or identifying cases of cyber-bulling. Given this context, the objective of our research is to contribute to the improvement of emotion recognition task through an automatic technique focused on reducing both the time and cost needed to develop emotion corpora. Our proposal is to exploit a bootstrapping approach based on intensional learning for automatic annotations with two main steps: 1) an initial similarity-based categorization where a set of seed sentences is created and extended by distributional semantic similarity (word vectors or word embeddings); 2) train a supervised classifier on the initially categorized set. The technique proposed allows us an efficient annotation of a large amount of emotion data with standards of reliability according to the evaluation results.