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


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(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

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Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Transfer Learning for Facial Emotion Recognition on Small Datasets
Paolo Barile, Clara Bassano, Paolo Piciocchi
(pages: 1-5)

How to Link Educational Purposes and Immersive Video Games Development? An Ontological Approach Proposal
Nathan Aky
(pages: 6-13)

Application of Building Information Modeling (BIM) in the Planning and Construction of a Building
Renata Maria Abrantes Baracho, Luiz Gustavo da Silva Santiago, Antonio Tagore Assumpção Mendoza e Silva, Marcelo Franco Porto
(pages: 14-19)

Transformative, Transdisciplinary, Transcendent Digital Education: Synergy, Sustainability and Calamity
Rusudan Makhachashvili, Ivan Semenist
(pages: 20-27)

New Online Tools for the Data Visualization of Bivalve Molluscs' Production Areas of Veneto Region
Eleonora Franzago, Claudia Casarotto, Matteo Trolese, Marica Toson, Mirko Ruzza, Manuela Dalla Pozza, Grazia Manca, Giuseppe Arcangeli, Nicola Ferrè, Laura Bille
(pages: 28-32)

Geodata Processing Methodology on GIS Platforms When Creating Spatial Development Plans of Territorial Communities: Case of Ukraine
Olena Kopishynska, Yurii Utkin, Ihor Sliusar, Leonid Flehantov, Mykola Somych, Oksana Yakovlieva, Olena Scryl
(pages: 33-40)

D-CIDE: An Interactive Code Learning Program
Lukas Grant, Matthew F. Tennyson, Jason Owen
(pages: 41-46)

Interdisciplinary Digital Skills Development for Educational Communication: Emergency and Ai-Enhanced Digitization
Rusudan Makhachashvili, Ivan Semenist, Ganna Prihodko, Irina Kolegaeva, Olexandra Prykhodchenko, Olena Tupakhina
(pages: 47-51)

Interdisciplinarity in Smart Systems Applied to Rural School Transport in Brazil
Renata Maria Abrantes Baracho, Mozart Joaquim Magalhães Vidigal, Marcelo Franco Porto, Beatriz Couto
(pages: 52-59)

Peculiarities of the Realization of IT Projects for the Implementation of ERP Systems on the Path of Digitalization of Territorial Communities Activities
Olena Kopishynska, Yurii Utkin, Ihor Sliusar, Khanlar Makhmudov, Olena Kalashnyk, Svitlana Moroz, Olena Kyrychenko
(pages: 60-67)


 

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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