Microblog Emotion Classification by Computing Similarity in Text, Time, and Space

Anja Summa, Bernd Resch, Michael Strube


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
Venues:
PEOPLES | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
153–162
Language:
URL:
https://www.aclweb.org/anthology/W16-4317
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-4317.pdf