Application of soft computing techniques for estimating emotional states expressed in Twitter ® time series data
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Dosyalar
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer London
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Because the emotional states of selected social groups may constitute a complex phenomenon, a suitable methodology is needed to analyze Twitter ® text data that can reflect social emotions. Understanding the nature of social barometer data in terms of its underlying dynamics is critical for predicting the future states or behaviors of large social groups. This study investigated the use of the supervised soft computing techniques (1) fuzzy time series (FTS), (2) artificial neural network (ANN)-based FTS, and (3) adaptive neuro-fuzzy inference systems (ANFIS) for predicting the emotional states expressed in Twitter ® data. The examined dataset contained 25,952 data points reflecting more than 380,000 Twitter ® messages recorded hourly. The model prediction accuracy was performed using the root-mean-square error. The ANFIS approach resulted in the most accurate prediction among the three examined soft computing approaches. The findings of the study showed that the FTS, ANN-based FTS, and ANFIS models could be used to predict the emotional states of a large social group based on historical data. Such a modeling approach can support the development of real-time social and emotional awareness for practical decision-making, as well as rapid socio-cultural assessment and training. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
Açıklama
Anahtar Kelimeler
Adaptive Neuro-Fuzzy İnference Systems, Artificial Neural Networks, Fuzzy Time Series, Human Emotional States, Twitter ®
Kaynak
Neural Computing and Applications
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
