Application of soft computing techniques for estimating emotional states expressed in Twitter (R) time series data

ÇAKIT E., Karwowski W., Servi L.

NEURAL COMPUTING & APPLICATIONS, cilt.32, sa.8, ss.3535-3548, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 8
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s00521-019-04048-5
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Sayfa Sayıları: ss.3535-3548
  • Anahtar Kelimeler: Human emotional states, Adaptive neuro-fuzzy inference systems, Artificial neural networks, Fuzzy time series, Twitter (R), FORECASTING ENROLLMENTS, FUZZY
  • Gazi Üniversitesi Adresli: Evet

Özet

Because the emotional states of selected social groups may constitute a complex phenomenon, a suitable methodology is needed to analyze Twitter(R) 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(R) data. The examined dataset contained 25,952 data points reflecting more than 380,000 Twitter(R) 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.