The Social Mood of News: Self-reported Annotations to Design Automatic Mood Detection Systems

Firoj Alam, Fabio Celli, Evgeny A. Stepanov, Arindam Ghosh, Giuseppe Riccardi


Abstract
In this paper, we address the issue of automatic prediction of readers’ mood from newspaper articles and comments. As online newspapers are becoming more and more similar to social media platforms, users can provide affective feedback, such as mood and emotion. We have exploited the self-reported annotation of mood categories obtained from the metadata of the Italian online newspaper corriere.it to design and evaluate a system for predicting five different mood categories from news articles and comments: indignation, disappointment, worry, satisfaction, and amusement. The outcome of our experiments shows that overall, bag-of-word-ngrams perform better compared to all other feature sets; however, stylometric features perform better for the mood score prediction of articles. Our study shows that self-reported annotations can be used to design automatic mood prediction systems.
Anthology ID:
W16-4316
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:
143–152
Language:
URL:
https://www.aclweb.org/anthology/W16-4316
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-4316.pdf