Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


Re-Published in
Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Transdisciplinary Communication as a Meta-Framework of Digital Education
Rusudan Makhachashvili, Ivan Semenist
(pages: 1-6)

Multidisciplinary Learning Using Online Networking in Biomedical Engineering
Shigehiro Hashimoto
(pages: 7-12)

Augmented Intelligence for Advancing Healthcare
Mohammad Ilyas
(pages: 13-19)

A Transdisciplinary Approach to Refereeal
Russell Jay Hendel
(pages: 20-25)

The Impact of Convictions on Interlocking Systems
Teresa Henkle Langness
(pages: 26-33)

Collaborative Convergence: Finding the Language for Trans-Disciplinary Communication to Occur
Cristo Leon, James Lipuma
(pages: 34-37)

Bridging the Gap Between the World of Education and the World of Business via Standards to Develop Competences of the Future at Universities
Paweł Poszytek
(pages: 38-42)

Multidisciplinary Learning for Multifaceted Thinking in Globalized Society
Shigehiro Hashimoto
(pages: 43-48)

From Spirituality to Technontology in Education
Florent Pasquier
(pages: 49-52)

Differentiated Learning and Digital Game Based Learning: The KIDEDU Project
Eleni Tsami
(pages: 53-57)

Emerging Role of Artificial Intelligence
Mohammad Ilyas
(pages: 58-65)

Practicing Transdisciplinarity and Trans-Domain Approaches in Education: Theory of and Communication in Values and Knowledge Education (VaKE)
Jean-Luc Patry
(pages: 66-71)

Reflexive Practice for Inter and Trans Disciplinary Research in the Third Millennium
Maria Grazia Albanesi
(pages: 72-76)


 

Abstracts

 


ABSTRACT


Real-Time Sentimental Polarity Classification on Live Social-Media

Khalid N. Alhayyan, Imran Ahmad


In recent years, the popularity of social media networks has attracted the attention of researchers, government agencies, politicians and business world alike, as a powerful platform to explore real-time trends. The data generated by these networks offers an opportunity to investigate people's behaviors and activities, but the high velocity and low quality of this data poses some unique challenges. Twitter, an example of social media networks, is particularly popular for this purpose due to its easily accessible API that is open to use for research purposes. Different techniques can be used to analyze patterns from available data. One of such techniques for extracting subjective information from any text such as opinions on various topics is Sentiment Polarity Classification, which quantifies emotions embedded in texts and classifies them as positive, negative or neutral. The focus of this paper is on preparing and analyzing real-time twitter streams to detect real-time trends on a particular topic using Sentiment Polarity Classification. We have used StreamSensing approach and have performed a supervised machine learning on real-time high velocity data using Apache Spark micro-batching technology to classify the opinions and feelings of people in real-time. Appropriate experiments for processing high rate of incoming streams have been carefully designed and conducted on live twitter data. The outcomes of these experiments were analyzed and presented. The findings of this paper fell into two perspectives: theoretical and practical. The theoretical perspective is seen in testing and confirming the validity of StreamSensing approach as well as the introduction of a sentimental polarity algorithm, while practically; this approach can be employed to perform trend analyses on any real-time streams related to live events.

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