Exploring the Applications of Artificial Intelligence in Marketing: A Topic Modelling Analysis

Paulo Rita et al.

Tourism & Management Studies2025https://doi.org/10.18089/tms.20250103article
ABDC C
Weight
0.44

What the paper says

Integrating Artificial Intelligence (AI) into marketing has revolutionised this field, offering new avenues of innovation and efficacy. This study aims to unveil the prevailing trends and patterns within the current academic literature about AI applications in Marketing and propose future research directions. The study conducted a comprehensive review by analysing 2,255 articles from the Scopus database from 1980 to 2022, employing a Text Mining approach and Latent Dirichlet Allocation (LDA) topic modelling. The findings suggest trends in research topics of Learning Models, Expert Systems, Natural Language Processing, Social Media, and Consumer Centricity. Outlier topics related to Health, Market Forecast, and Technology Impact on Youth have caught the researchers’ attention. The associated challenges and risks, ethical considerations, and practical implications were presented for each topic. This study develops literature on AI-driven marketing by unearthing the trend topics and suggesting a research agenda. At the same time, it offers actionable insights for businesses to enhance consumer engagement and ethical AI adoption in marketing practices.

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https://doi.org/https://doi.org/10.18089/tms.20250103

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@article{paulo2025,
  title        = {{Exploring the Applications of Artificial Intelligence in Marketing: A Topic Modelling Analysis}},
  author       = {Paulo Rita et al.},
  journal      = {Tourism & Management Studies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.18089/tms.20250103},
}

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.