Demonstrating the Reliability of Self-Annotated Emotion Data
Anton Malko, Cecile Paris, Andreas Duenser, Maria Kangas, Diego Molla, Ross Sparks, Stephen Wan
Abstract
Vent is a specialised iOS/Android social media platform with the stated goal to encourage people to post about their feelings and explicitly label them. In this paper, we study a snapshot of more than 100 million messages obtained from the developers of Vent, together with the labels assigned by the authors of the messages. We establish the quality of the self-annotated data by conducting a qualitative analysis, a vocabulary based analysis, and by training and testing an emotion classifier. We conclude that the self-annotated labels of our corpus are indeed indicative of the emotional contents expressed in the text and thus can support more detailed analyses of emotion expression on social media, such as emotion trajectories and factors influencing them.- Anthology ID:
- 2021.clpsych-1.5
- Volume:
- Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Nazli Goharian, Philip Resnik, Andrew Yates, Molly Ireland, Kate Niederhoffer, Rebecca Resnik
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 45–54
- Language:
- URL:
- https://aclanthology.org/2021.clpsych-1.5
- DOI:
- 10.18653/v1/2021.clpsych-1.5
- Cite (ACL):
- Anton Malko, Cecile Paris, Andreas Duenser, Maria Kangas, Diego Molla, Ross Sparks, and Stephen Wan. 2021. Demonstrating the Reliability of Self-Annotated Emotion Data. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access, pages 45–54, Online. Association for Computational Linguistics.
- Cite (Informal):
- Demonstrating the Reliability of Self-Annotated Emotion Data (Malko et al., CLPsych 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.clpsych-1.5.pdf