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

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dc.contributor.authorDashtipour, K.
dc.contributor.authorTaylor, W.
dc.contributor.authorAnsari, S.
dc.contributor.authorGogate, M.
dc.contributor.authorZahid, A.
dc.contributor.authorSambo, Y.
dc.contributor.authorHussain, A.
dc.contributor.authorAbbasi, Q. H.
dc.contributor.authorImran, M. A.
dc.date.accessioned2021-12-21T19:25:01Z
dc.date.available2021-12-21T19:25:01Z
dc.date.issued2021
dc.description.abstractWith 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.en_ES
dc.identifier.citationDashtipour, 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. En Front Big Data (Vol. 4, p. 640868). https://doi.org/10.3389/fdata.2021.640868en_ES
dc.identifier.issn2624-909x
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/3117
dc.language.isoen_USen_ES
dc.publisherFront Big Dataen_ES
dc.subject5Gen_ES
dc.subjectMACHINE LEARNINGen_ES
dc.subjectMOBILE NETWORK QUALITYen_ES
dc.subjectOPINIONen_ES
dc.subjectMININGen_ES
dc.subjectSENTIMENT ANALYSISen_ES
dc.titlePublic Perception of the Fifth Generation of Cellular Networks (5G) on Social Mediaen_ES
dc.typeArticuloen_ES
dc.ubicacionhttps://doi.org/10.3389/fdata.2021.640868en_ES
uv.catalogadorSGGen_ES
uv.colectionBibliografía 5Gen_ES

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