TY - JOUR
T1 - Computational Content Analysis in Advertising Research
AU - Barari, Mojtaba
AU - Eisend, Martin
N1 - Published online: 15 October 2024.
PY - 2024
Y1 - 2024
N2 - Computational content analysis (CCA) has experienced a surge in popularity in the field of advertising research. Despite advancements, a comprehensive methodology guide in this area is lacking, presenting challenges for researchers seeking to incorporate these techniques into their study design. This methodology paper aims to provide a thorough overview of CCA applied to different and multiple modalities, including text, images, audio, and video, as a guide for interested researchers. We outline the use of machine learning through CCA in advertising research, covering a wide range of supervised (classification, object detection, emotion analysis, audio sentiment analysis, regression) and unsupervised (topic modeling and clustering) machine learning methods, alongside conventional CCA methods (entity extraction and sentiment analysis). Additionally, we provide a future research agenda that demonstrates how researchers can utilize generative artificial intelligence in CCA.
AB - Computational content analysis (CCA) has experienced a surge in popularity in the field of advertising research. Despite advancements, a comprehensive methodology guide in this area is lacking, presenting challenges for researchers seeking to incorporate these techniques into their study design. This methodology paper aims to provide a thorough overview of CCA applied to different and multiple modalities, including text, images, audio, and video, as a guide for interested researchers. We outline the use of machine learning through CCA in advertising research, covering a wide range of supervised (classification, object detection, emotion analysis, audio sentiment analysis, regression) and unsupervised (topic modeling and clustering) machine learning methods, alongside conventional CCA methods (entity extraction and sentiment analysis). Additionally, we provide a future research agenda that demonstrates how researchers can utilize generative artificial intelligence in CCA.
U2 - 10.1080/00913367.2024.2407642
DO - 10.1080/00913367.2024.2407642
M3 - Review article
SN - 0091-3367
VL - 53
SP - 681
EP - 699
JO - Journal of Advertising
JF - Journal of Advertising
IS - 5
ER -