Application of soft computing techniques for estimating emotional states expressed in Twitter ® time series data

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dc.contributor.authorÇakıt, Erman
dc.contributor.authorKarwowski, Waldemar
dc.contributor.authorServi, Les
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-16T08:25:42Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-16T08:25:42Z
dc.date.issued2020
dc.departmentMühendislik Fakültesi
dc.description.abstractBecause 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.
dc.identifier.doi10.1007/s00521-019-04048-5
dc.identifier.issn0941-0643
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-019-04048-5
dc.identifier.urihttps://hdl.handle.net/20.500.12451/3080
dc.identifier.wosWOS:000524416400036
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer London
dc.relation.ispartofNeural Computing and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectAdaptive Neuro-Fuzzy İnference Systems
dc.subjectArtificial Neural Networks
dc.subjectFuzzy Time Series
dc.subjectHuman Emotional States
dc.subjectTwitter ®
dc.titleApplication of soft computing techniques for estimating emotional states expressed in Twitter ® time series data
dc.typeArticle

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