Evidence map›Paper›PMID 36643702›Full record

ArticleFrontiers in psychology2022

Public perceptions of digital fashion: An analysis of sentiment and Latent Dirichlet Allocation topic modeling.

Yixin Zou, Ding-Bang Luh, Shizhu Lu

Abstract read
In one paragraph

Article in Frontiers in psychology, 2022. 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

3 authors.

Yixin ZouSchool of Art and Design, Guangdong University of Technology, Guangzhou, China.
Ding-Bang LuhSchool of Art and Design, Guangdong University of Technology, Guangzhou, China.
Shizhu LuSchool of Art and Design, Guangdong University of Technology, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since digital technology has had a significant impact on the fashion industry, digital fashion has become a hot topic in today's society. Currently, research on digital fashion is focused on the transformation of enterprise marketing strategies and the discussion of digital technology. Despite this, the current study does not include an analysis of the audience's emotional and cognitive responses to digital fashion on social networking platforms. A comprehensive analysis and discussion of 52,891 posts about digital fashion and virtual fashion published on social networking sites was conducted using k-means clustering analysis, Latent Dirichlet Allocation (LDA) topic modeling, and sentiment analysis in this study. The study examines the public's perception and hot topics about digital fashion, as well as the industry's development situation and trends. According to the findings, both positive and neutral emotions accompany the public's attitude toward digital fashion. There is a wide range of topics covered in the discussion. Innovations in digital technology have impacted the creation of jobs, talent demand, marketing strategies, profit forms, and industrial chain innovation of fashion-related businesses. Researchers in related fields will find this study useful not only as a reference for research methods and directions, but also as a source of references for research methodology. A case study and data reference will also be provided to industry practitioners.

Indexed as

digital fashiondigital fashion trends(LDA) topic modelingNFTpublic perceptionssentiment analysisvirtual fashion

Identifiers

PMID36643702
PMCPMC9832026

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.