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

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Abstract

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.

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Acknowledgements

This study was supported in part by Grant No. N00014-11-1-0934 from the Office of Naval Research (ONR), Human, Social, Cultural, and Behavioral (HSCB) modeling program awarded to Waldemar Karwowski at the University of Central Florida, Orlando, Florida. Approved for Public Release; Distribution Unlimited. Case Number 17-3282 ©2017, The MITRE Corporation. All Rights Reserved.

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Authors and Affiliations

  1. Department of Industrial Engineering, Aksaray University, 68100, Aksaray, Turkey

    Erman Çakıt

  2. Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, 32816-2993, USA

    Waldemar Karwowski

  3. The MITRE Corporation, 202 Burlington Road, Bedford, MA, 01773, USA

    Les Servi

  4. Department of Industrial Engineering, Gazi University, 06570, Ankara, Turkey

    Erman Çakıt

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  1. Erman Çakıt
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  2. Waldemar Karwowski
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  3. Les Servi
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Correspondence to Erman Çakıt.

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Çakıt, E., Karwowski, W. & Servi, L. Application of soft computing techniques for estimating emotional states expressed in Twitter® time series data. Neural Comput & Applic 32, 3535–3548 (2020). https://doi.org/10.1007/s00521-019-04048-5

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