Sentiment Analysis of Semantically Interoperable Social Media Platforms Using Computational Intelligence Techniques

Author:

Alqahtani Ali1ORCID,Khan Surbhi Bhatia23ORCID,Alqahtani Jarallah4ORCID,AlYami Sultan4ORCID,Alfayez Fayez5

Affiliation:

1. Department of Networks and Communications Engineering, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia

2. Department of Data Science, School of Science, Engineering and Environment, University of Salford, Salford M5 4WT, UK

3. Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon

4. Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia

5. Department of Computer Science and Information, College of Science, Majmaah Univesity, Al Majma’ah 11952, Saudi Arabia

Abstract

Competitive intelligence in social media analytics has significantly influenced behavioral finance worldwide in recent years; it is continuously emerging with a high growth rate of unpredicted variables per week. Several surveys in this large field have proved how social media involvement has made a trackless network using machine learning techniques through web applications and Android modes using interoperability. This article proposes an improved social media sentiment analytics technique to predict the individual state of mind of social media users and the ability of users to resist profound effects. The proposed estimation function tracks the counts of the aversion and satisfaction levels of each inter- and intra-linked expression. It tracks down more than one ontologically linked activity from different social media platforms with a high average success rate of 99.71%. The accuracy of the proposed solution is 97% satisfactory, which could be effectively considered in various industrial solutions such as emo-robot building, patient analysis and activity tracking, elderly care, and so on.

Funder

Deputy for Research and Innovation—Ministry of Education, Kingdom of Saudi Arabia

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference36 articles.

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