Identifying the sentiment styles of YouTube’s vloggers

Bennett Kleinberg, Maximilian Mozes, Isabelle van der Vegt


Abstract
Vlogs provide a rich public source of data in a novel setting. This paper examined the continuous sentiment styles employed in 27,333 vlogs using a dynamic intra-textual approach to sentiment analysis. Using unsupervised clustering, we identified seven distinct continuous sentiment trajectories characterized by fluctuations of sentiment throughout a vlog’s narrative time. We provide a taxonomy of these seven continuous sentiment styles and found that vlogs whose sentiment builds up towards a positive ending are the most prevalent in our sample. Gender was associated with preferences for different continuous sentiment trajectories. This paper discusses the findings with respect to previous work and concludes with an outlook towards possible uses of the corpus, method and findings of this paper for related areas of research.
Anthology ID:
D18-1394
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3581–3590
Language:
URL:
https://aclanthology.org/D18-1394
DOI:
10.18653/v1/D18-1394
Bibkey:
Cite (ACL):
Bennett Kleinberg, Maximilian Mozes, and Isabelle van der Vegt. 2018. Identifying the sentiment styles of YouTube’s vloggers. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3581–3590, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Identifying the sentiment styles of YouTube’s vloggers (Kleinberg et al., EMNLP 2018)
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PDF:
https://aclanthology.org/D18-1394.pdf
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