Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition

Yi-jie Tang, Hsin-Hsi Chen


Abstract
The conversations between posters and repliers in microblogs form a valuable writer-reader emotion corpus. This paper adopts a log relative frequency ratio to investigate the linguistic features which affect emotion transitions, and applies the results to predict writers' and readers' emotions. A 4-class emotion transition predictor, a 2-class writer emotion predictor, and a 2-class reader emotion predictor are proposed and compared.
Anthology ID:
L12-1007
Volume:
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)
Month:
May
Year:
2012
Address:
Istanbul, Turkey
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Mehmet Uğur Doğan, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
1226–1229
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/117_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Yi-jie Tang and Hsin-Hsi Chen. 2012. Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 1226–1229, Istanbul, Turkey. European Language Resources Association (ELRA).
Cite (Informal):
Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition (Tang & Chen, LREC 2012)
Copy Citation:
PDF:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/117_Paper.pdf