Analysis of Human Behavior by Mining Textual Data: Current Research Topics and Analytical Techniques

Edgar Gutierrez,Krzysztof Fiok,Mohammad Reza Davahli,Waldemar Karwowski,Tareq Ahram,Tameika Liciaga

doi:10.3390/sym13071276

Abstract

The goal of this study was to conduct a literature review of current approaches and techniques for identifying, understanding, and predicting human behaviors through mining a variety of sources of textual data with a focus on enabling classification of psychological behaviors regarding emotion, cognition, and social empathy. This review was performed using keyword searches in ISI Web of Science, Engineering Village Compendex, ProQuest Dissertations, and Google Scholar. Our findings show that, despite recent advancements in predicting human behaviors based on unstructured textual data, significant developments in data analytics systems for identification, determination of interrelationships, and prediction of human cognitive, emotional and social behaviors remain lacking.

Highlights

At present, the vast amount of textual data being generated from myriad sources is rapidly increasing [1]
The results indicated that more than 50% of the reviewed literature was completed by using natural language processing (NLP), which was one of the strongest approaches
We analyzed published articles on different topics related to text mining and human behavior

Summary

Introduction

The vast amount of textual data being generated from myriad sources (e.g., formal or informal reports, interviews, call logs, emails, performance documents, blogs, tweets, comments, or social media entries) is rapidly increasing [1] This increase in textual data allows for large repositories to be analyzed, summarized, and deciphered, using these data to make insightful decisions has become much more challenging. Mining textual data can provide deep insights into an individual’s views, attitudes, sentiments, and emotions toward other individuals and help predict future social behaviors [2] Such human behaviors can be identified and understood by extracting textual data with meaningful semantic properties, including metadata such as concepts, events, keywords, categories, including symmetric and asymmetric relationships. According to Bornstein et al [4], human behavior is described as “the potential and expressed capacity for physical, mental, and social activity during the phases of human life.” Regarding the identification of behaviors by text mining, Tausczik et al [5] stated that “by drawing on massive amounts of text, researchers can begin to link everyday language use with behavioral and self-reported measures of personality, social behavior, and cognitive styles.” Pennebaker and

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Conclusion