S S symmetry Review Identification and Prediction of Human Behavior through Mining of Unstructured Textual Data Mohammad Reza Davahli 1,*, Waldemar Karwowski 1 , Edgar Gutierrez 1,2 , Krzysztof Fiok 1 , Grzegorz Wróbel 3, Redha Taiar 4 and Tareq Ahram 1 1 Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL 32816, USA;
[email protected] (W.K.);
[email protected] (E.G.); fi
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[email protected] (T.A.) 2 Center for Latin-American Logistics Innovation, LOGyCA, Bogota 110111, Colombia 3 Department of Logistics and Process Engineering, University of Information Technology and Management in Rzeszów, 35-225 Rzeszów, Poland;
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[email protected] Received: 30 September 2020; Accepted: 17 November 2020; Published: 19 November 2020 Abstract: The identification of human behavior can provide useful information across multiple job spectra. Recent advances in applying data-based approaches to social sciences have increased the feasibility of modeling human behavior. In particular, studying human behavior by analyzing unstructured textual data has recently received considerable attention because of the abundance of textual data. The main objective of the present study was to discuss the primary methods for identifying and predicting human behavior through the mining of unstructured textual data. Of the 823 articles analyzed, 87 met the predefined inclusion criteria and were included in the literature review. Our results show that the included articles could be symmetrically classified into two groups. The first group of articles attempted to identify the leading indicators of human behavior in unstructured textual data.