Evidence map›Paper›PMID 34240048›Full record

ArticleFrontiers in big data2021

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

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in big data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Kia DashtipourJames Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom.
William TaylorJames Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom.
Shuja AnsariJames Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom.
Mandar GogateSchool of Computing, Edinburgh Napier University, Edinburgh, United Kingdom.
Adnan ZahidJames Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom.
Yusuf SamboJames Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom.
Amir HussainSchool of Computing, Edinburgh Napier University, Edinburgh, United Kingdom.
Qammer H AbbasiJames Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom.
Muhammad Ali ImranJames Watt School of Engineering, University of Glasgow, Glasgow, United Kingdom.

Funding

Chief Scientist Office COV/NAP/20/07
6 · The paper itself

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.

Indexed as

5Gmachine learningmobile network qualityopinion miningsentiment analysis

Identifiers

PMID34240048
PMCPMC8259739

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.