Mahiye Uluyagmur Ozturk^a, Ayse Rodopman Arman^b, Gresa Carkaxhiu Bulut^c, Onur Tugce Poyraz Findik^b, Sultan Seval Yilmaz^d, Herdem Aslan Genc^b, M. Yanki Yazgan^e,f, Umut Teker^a, Zehra Cataltepe^a

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 891-905, 2018, DOI:10.31209/2018.100000058

Abstract Emotion recognition behavior and performance may vary between people with major neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these differences for early diagnosis and individual treatment purposes. This study represents a methodology by using statistical data analysis and machine learning to provide help to psychiatrists and therapists on the diagnosis and individualized treatment of participants with ASD and ADHD. In this paper we propose an emotion recognition experiment environment and collect eye tracker fixation data together with the application log data (APL). In order to detect… More >

Jinseok Woo, János Botzheim, Naoyuki Kubota

Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 829-841, 2018, DOI:10.1080/10798587.2017.1364919

Abstract This paper introduces the integrated system of a smart-device-based cognitive robot partner called iPhonoid-C. Interaction with a robot partner requires many elements, including verbal communication, nonverbal communication, and embodiment as well. A robot partner should be able to understand human sentences, as well as nonverbal information such as human gestures. In the proposed system, the robot has an emotional model connecting the input information from the human with the robot’s behavior. Since emotions are involved in human natural communication, and emotion has a significant impact on humans’ actions, it is important to develop an emotional model for the robot partner… More >

Wan Tao^a,b, Tao Liu^a,b

Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 65-72, 2018, DOI:10.1080/10798587.2016.1267243

Abstract With the explosive growth of various social media applications, individuals and organizations are increasingly using their contents (e.g. reviews, forum discussions, blogs, micro-blogs, comments, and postings in social network sites) for decision-making. These contents are typical big data. Opinion mining or sentiment analysis focuses on how to extract emotional semantics from these big data to help users to get a better decision. That is not an easy task, because it faces many problems, such as different context may make the meaning of the same word change variously, at the same time multilingual environment restricts the full use of the analysis… More >

Yixian Zhang¹, Jieren Cheng^{2, *}, Yifan Yang², Haocheng Li², Xinyi Zheng², Xi Chen², Boyi Liu³, Tenglong Ren⁴, Naixue Xiong⁵

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1415-1434, 2020, DOI:10.32604/cmc.2020.011316

Abstract With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed. Establish a “Scrapy-Redis-Bloomfilter” distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the… More >

Muhammad Zubair Asghar¹, Fazli Subhan², Muhammad Imran¹, Fazal Masud Kundi¹, Adil Khan³, Shahboddin Shamshirband^{4, 5, *}, Amir Mosavi^{6, 7, 8}, Peter Csiba⁸, Annamaria R. Varkonyi Koczy⁸

CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1093-1118, 2020, DOI:10.32604/cmc.2020.07709

Abstract Emotion detection from the text is a challenging problem in the text analytics. The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine… More >

Sebastian Udholm¹, Camilla Nyboe¹, Thomas Meinertz Dantoft², Torben Jørgensen^2,3,4, Charlotte U. Rask⁵, Vibeke E. Hjortdal¹

Congenital Heart Disease, Vol.14, No.5, pp. 803-810, 2019, DOI:10.1111/chd.12808

Abstract Objective: For the first time, we wish to assess the psychiatric burden in adult patients living with small, unrepaired atrial septal defects (ASD) using register‐based data, com‐ bined with self‐reported measures on levels emotional distress and educational status.
Design: A descriptive study using both the unique Danish registries and validated psychiatric questionnaires and scales, including: The Symptom Checklist, Whiteley‐7, and Brief Illness Perception Questionnaire.
Patients: Adult patients with small, unrepairedASD, diagnosed between 1953 and 2011.
Outcome Measures: Number of register‐based psychiatric diagnoses. Additionally, symptoms of anxiety, depression, somatization, health anxiety, illness perception, and levels of educational attainment compared to age‐… More >

Zijing Wang¹, Min (Maggie) Wan², Huaying Wang^3,*, Yuchen Wei⁴

International Journal of Mental Health Promotion, Vol.20, No.1, pp. 1-13, 2018, DOI:10.32604/IJMHP.2018.010732

Abstract Workplace deviance is an important problem in organization management. Previous studies focused too much on the influence of various factors in the workplace and ignored the interference of family factors. We integrate emotional social function theory and emotional labor theory, and examine the effect of (in) congruence between work and family status on workplace deviance. Using longitudinal data and polynomial regression, we find that: (1) Emotional exhaustion is higher when work and family status are congruent; (2) In the case of work-family congruence, emotional exhaustion is higher when work and family status are aligned at a low level than when… More >