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Public perception of the fifth generation of cellular networks (5G) on social media

Dashtipour, Kia; Taylor, William; Ansari, Shuja; Gogate, Mandar; Zahid, Adnan; Sambo, Yusuf; Hussain, Amir; Abbasi, Qammer H; Imran, Muhammad Ali

Authors

William Taylor

Shuja Ansari

Adnan Zahid

Yusuf Sambo

Qammer H Abbasi

Muhammad Ali Imran



Abstract

With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these types of reviews. To utilize these reviews for user perception, there is a need for automated tools that can process online user data. In this paper, a sentiment analysis framework has been proposed to identify people’s perception towards mobile networks. The proposed framework consists of three basic steps: preprocessing, feature selection, and applying different machine learning algorithms. The performance of the framework has taken into account different feature combinations. The simulation results show that the best performance is by integrating unigram, bigram, and trigram features.

Citation

Dashtipour, K., Taylor, W., Ansari, S., Gogate, M., Zahid, A., Sambo, Y., Hussain, A., Abbasi, Q. H., & Imran, M. A. (2021). Public perception of the fifth generation of cellular networks (5G) on social media. Frontiers in Big Data, 4, Article 640868. https://doi.org/10.3389/fdata.2021.640868

Journal Article Type Article
Acceptance Date May 4, 2021
Online Publication Date Jun 18, 2021
Publication Date 2021
Deposit Date Jul 22, 2021
Publicly Available Date Jul 22, 2021
Journal Frontiers in Big Data
Print ISSN 2624-909X
Electronic ISSN 2624-909X
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 4
Article Number 640868
DOI https://doi.org/10.3389/fdata.2021.640868
Keywords sentiment analysis, 5G, mobile network quality, machine learning, opinion mining
Public URL http://researchrepository.napier.ac.uk/Output/2788415

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Public Perception Of The Fifth Generation Of Cellular Networks (5G) On Social Media (1.1 Mb)
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 2021 Dashtipour, Taylor, Ansari, Gogate, Zahid, Sambo, Hussain, Abbasi and Imran. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.





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