The Challenges of Multi-dimensional Sentiment Analysis Across Languages

Emily Öhman, Timo Honkela, Jörg Tiedemann

Abstract
This paper outlines a pilot study on multi-dimensional and multilingual sentiment analysis of social media content. We use parallel corpora of movie subtitles as a proxy for colloquial language in social media channels and a multilingual emotion lexicon for fine-grained sentiment analyses. Parallel data sets make it possible to study the preservation of sentiments and emotions in translation and our assessment reveals that the lexical approach shows great inter-language agreement. However, our manual evaluation also suggests that the use of purely lexical methods is limited and further studies are necessary to pinpoint the cross-lingual differences and to develop better sentiment classifiers.
Anthology ID:
W16-4315
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:
138–142
Language:
URL:
https://aclanthology.org/W16-4315
DOI:
Bibkey:
Cite (ACL):
Emily Öhman, Timo Honkela, and Jörg Tiedemann. 2016. The Challenges of Multi-dimensional Sentiment Analysis Across Languages. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 138–142, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
The Challenges of Multi-dimensional Sentiment Analysis Across Languages (Öhman et al., PEOPLES 2016)
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