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Creating and analysing a multimodal corpus of news texts with Google Cloud Vision's automatic image tagger

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Creating and analysing a multimodal corpus of news texts with Google Cloud Vision's automatic image tagger. / Baker, Paul; Collins, Luke.
In: Applied Corpus Linguistics, Vol. 3, No. 1, 100043, 30.04.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Baker, P & Collins, L 2023, 'Creating and analysing a multimodal corpus of news texts with Google Cloud Vision's automatic image tagger', Applied Corpus Linguistics, vol. 3, no. 1, 100043. https://doi.org/10.1016/j.acorp.2023.100043

APA

Baker, P., & Collins, L. (2023). Creating and analysing a multimodal corpus of news texts with Google Cloud Vision's automatic image tagger. Applied Corpus Linguistics, 3(1), Article 100043. https://doi.org/10.1016/j.acorp.2023.100043

Vancouver

Baker P, Collins L. Creating and analysing a multimodal corpus of news texts with Google Cloud Vision's automatic image tagger. Applied Corpus Linguistics. 2023 Apr 30;3(1):100043. Epub 2023 Feb 2. doi: 10.1016/j.acorp.2023.100043

Author

Baker, Paul ; Collins, Luke. / Creating and analysing a multimodal corpus of news texts with Google Cloud Vision's automatic image tagger. In: Applied Corpus Linguistics. 2023 ; Vol. 3, No. 1.

Bibtex

@article{b12a27456f9b45c2a7c74208c17835b7,
title = "Creating and analysing a multimodal corpus of news texts with Google Cloud Vision's automatic image tagger",
abstract = "This study describes the creation and analysis of a small multimodal corpus of British news articles about obesity, where tags were assigned to images in the articles using the automatic tagger Google Cloud Vision. In order to illustrate the potential for analysis of image tags, the corpus analysis tool WordSmith was used to identify differences between newspapers in the ways that obesity was framed. Three forms of analysis were carried out – the first simply compared keywords across the newspapers, the second examined key visual tags and their collocates associated with each newspaper, while the third incorporated a combined analysis of words and image tags. The three analyses produced complementary findings, indicating the value in using Google Cloud Vision in creating and analysing multimodal corpora. The paper ends by reflecting on the method undertaken, while considering how additional research could improve our understanding of image tagging.",
keywords = "news, annotation, visual, Image, obesity, discourse",
author = "Paul Baker and Luke Collins",
year = "2023",
month = apr,
day = "30",
doi = "10.1016/j.acorp.2023.100043",
language = "English",
volume = "3",
journal = "Applied Corpus Linguistics",
issn = "2666-7991",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Creating and analysing a multimodal corpus of news texts with Google Cloud Vision's automatic image tagger

AU - Baker, Paul

AU - Collins, Luke

PY - 2023/4/30

Y1 - 2023/4/30

N2 - This study describes the creation and analysis of a small multimodal corpus of British news articles about obesity, where tags were assigned to images in the articles using the automatic tagger Google Cloud Vision. In order to illustrate the potential for analysis of image tags, the corpus analysis tool WordSmith was used to identify differences between newspapers in the ways that obesity was framed. Three forms of analysis were carried out – the first simply compared keywords across the newspapers, the second examined key visual tags and their collocates associated with each newspaper, while the third incorporated a combined analysis of words and image tags. The three analyses produced complementary findings, indicating the value in using Google Cloud Vision in creating and analysing multimodal corpora. The paper ends by reflecting on the method undertaken, while considering how additional research could improve our understanding of image tagging.

AB - This study describes the creation and analysis of a small multimodal corpus of British news articles about obesity, where tags were assigned to images in the articles using the automatic tagger Google Cloud Vision. In order to illustrate the potential for analysis of image tags, the corpus analysis tool WordSmith was used to identify differences between newspapers in the ways that obesity was framed. Three forms of analysis were carried out – the first simply compared keywords across the newspapers, the second examined key visual tags and their collocates associated with each newspaper, while the third incorporated a combined analysis of words and image tags. The three analyses produced complementary findings, indicating the value in using Google Cloud Vision in creating and analysing multimodal corpora. The paper ends by reflecting on the method undertaken, while considering how additional research could improve our understanding of image tagging.

KW - news

KW - annotation

KW - visual

KW - Image

KW - obesity

KW - discourse

U2 - 10.1016/j.acorp.2023.100043

DO - 10.1016/j.acorp.2023.100043

M3 - Journal article

VL - 3

JO - Applied Corpus Linguistics

JF - Applied Corpus Linguistics

SN - 2666-7991

IS - 1

M1 - 100043

ER -