Research Output
Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media
  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.

  • Type:

    Article

  • Date:

    18 June 2021

  • Publication Status:

    Published

  • Publisher

    Frontiers Media SA

  • DOI:

    10.3389/fdata.2021.640868

  • Cross Ref:

    10.3389/fdata.2021.640868

  • ISSN:

    2624-909X

  • Funders:

    EPSRC Engineering and Physical Sciences Research Council

Citation

Dashtipour, K., Taylor, W., Ansari, S., Gogate, M., Zahid, A., Sambo, Y., …Imran, M. A. (2021). Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media. Frontiers in Big Data, 4, https://doi.org/10.3389/fdata.2021.640868

Authors

Keywords

sentiment analysis, 5G, mobile network quality, machine learning, opinion mining

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    © 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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