Abstract
Most work in NLP analysing microblogs focuses on textual content thus neglecting temporal and spatial information. We present a new interdisciplinary method for emotion classification that combines linguistic, temporal, and spatial information into a single metric. We create a graph of labeled and unlabeled tweets that encodes the relations between neighboring tweets with respect to their emotion labels. Graph-based semi-supervised learning labels all tweets with an emotion.- Anthology ID:
- W16-4317
- Volume:
- Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Malvina Nissim, Viviana Patti, Barbara Plank
- Venue:
- PEOPLES
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 153–162
- Language:
- URL:
- https://aclanthology.org/W16-4317
- DOI:
- Cite (ACL):
- Anja Summa, Bernd Resch, and Michael Strube. 2016. Microblog Emotion Classification by Computing Similarity in Text, Time, and Space. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 153–162, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Microblog Emotion Classification by Computing Similarity in Text, Time, and Space (Summa et al., PEOPLES 2016)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W16-4317.pdf