TY - GEN
T1 - Modelling valence and arousal in facebook posts
AU - Preoţiuc-Pietro, Daniel
AU - Schwartz, H. Andrew
AU - Park, Gregory
AU - Eichstaedt, Johannes C.
AU - Kern, Margaret
AU - Ungar, Lyle
AU - Shulman, Elizabeth P.
N1 - Publisher Copyright: © 2016 Association for Computational Linguistics
PY - 2016
Y1 - 2016
N2 - Access to expressions of subjective personal posts increased with the popularity of Social Media. However, most of the work in sentiment analysis focuses on predicting only valence from text and usually targeted at a product, rather than affective states. In this paper, we introduce a new data set of 2895 Social Media posts rated by two psychologically-trained annotators on two separate ordinal nine-point scales. These scales represent valence (or sentiment) and arousal (or intensity), which defines each post's position on the circumplex model of affect, a well-established system for describing emotional states (Russell, 1980; Posner et al., 2005). The data set is used to train prediction models for each of the two dimensions from text which achieve high predictive accuracy - correlated at r =.65 with valence and r =.85 with arousal annotations. Our data set offers a building block to a deeper study of personal affect as expressed in social media. This can be used in applications such as mental illness detection or in automated large-scale psychological studies.
AB - Access to expressions of subjective personal posts increased with the popularity of Social Media. However, most of the work in sentiment analysis focuses on predicting only valence from text and usually targeted at a product, rather than affective states. In this paper, we introduce a new data set of 2895 Social Media posts rated by two psychologically-trained annotators on two separate ordinal nine-point scales. These scales represent valence (or sentiment) and arousal (or intensity), which defines each post's position on the circumplex model of affect, a well-established system for describing emotional states (Russell, 1980; Posner et al., 2005). The data set is used to train prediction models for each of the two dimensions from text which achieve high predictive accuracy - correlated at r =.65 with valence and r =.85 with arousal annotations. Our data set offers a building block to a deeper study of personal affect as expressed in social media. This can be used in applications such as mental illness detection or in automated large-scale psychological studies.
UR - https://www.scopus.com/pages/publications/85111056155
M3 - Conference contribution
T3 - Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016
SP - 9
EP - 15
BT - Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics
A2 - Balahur, Alexandra
A2 - van der Goot, Erik
A2 - Vossen, Piek
A2 - Montoyo, Andres
PB - Association for Computational Linguistics (ACL)
T2 - 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016
Y2 - 16 July 2016
ER -