METHOD OF GENERATING ADVERTISING IMAGES BASED ON KEYWORDS
DOI: 10.31673/2412-4338.2023.043745
Abstract
This study is dedicated to a method of generating advertisement imagery based on the usage of keywords. Within this method, algorithms of artificial intelligence, machine learning, and natural language processing are employed to create visual content that aligns with specific requirements and goals of an advertising campaign.
The paper delves into the core steps of this process. The determination of keywords implies selecting the most relevant terms that represent the essence of the advertised product or service. These keywords then undergo profound analysis with natural language processing techniques that allow identifying their semantic and contextual relationships.
The subsequent step involves associative analysis, through which the system identifies visual elements most frequently associated with the provided keywords. This information becomes the basis for generating images with machine learning algorithms. The end product - the generated image - is assessed for correspondence with the keywords and the overall goals of the advertising campaign. If the result complies with all requirements, the image is then used in the advertising campaign. The study's findings indicate that the presented method is an effective tool for creating precise and targeted visual content. This approach opens up new opportunities for the advertising industry, enabling the automation of the content creation process and focusing on strategic planning.
In the future, this work can serve as a platform for further research directed at enhancing natural language processing models, expanding the image database, and improving image generation algorithms. This will lead to increased accuracy and efficiency in creating advertisement content, marking a promising direction in the field of digital marketing.
Keywords: method, generation, advertisement image, keywords, artificial intelligence, machine learning, natural language processing.
References:
1. Schweidel, D. A., et al. (2023). Leveraging AI for Content Generation: A Customer Equity Perspective. Review of Marketing Research, 125-145. https://doi.org/10.1108/s1548-643520230000020006
2. Vlačić, B., et al. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187-203. https://doi.org/10.1016/j.jbusres.2021.01.055
3. Paschen, J., Wilson, M., & Ferreira, J. J. (2020). Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel. Business Horizons, 63(3), 403-414. https://doi.org/10.1016/j.bushor.2020.01.003
4. Bawack, R. E., et al. (2022). Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electronic Markets. https://doi.org/10.1007/s12525-022-00537-z
5. Rodgers, W., & Nguyen, T. (2022). Advertising Benefits from Ethical Artificial Intelligence Algorithmic Purchase Decision Pathways. Journal of Business Ethics. https://doi.org/10.1007/s10551-022-05048-7
6. Campbell, C., et al. (2021). Preparing for an Era of Deepfakes and AI-Generated Ads: A Framework for Understanding Responses to Manipulated Advertising. Journal of Advertising, 1-17. https://doi.org/10.1080/00913367.2021.1909515
7. Rusthollkarhu, S., et al. (2022). Managing B2B customer journeys in digital era: Four management activities with artificial intelligence-empowered tools. Industrial Marketing Management, 104, 241-257. https://doi.org/10.1016/j.indmarman.2022.04.014
8. Cao, Y., et al. (2023). A comprehensive survey of ai-generated content (aigc): A history of generative ai from gan to chatgpt. arXiv preprint. arXiv:2303.04226.
9. Guo, S., et al. (2021). Vinci: An Intelligent Graphic Design System for Generating Advertising Posters. CHI '21: CHI Conference on Human Factors in Computing Systems, Yokohama Japan. https://doi.org/10.1145/3411764.3445117
10. Zheng, X., et al. (2019). Content-aware generative modeling of graphic design layouts. ACM Transactions on Graphics, 38(4), 1-15. https://doi.org/10.1145/3306346.3322971
11. Lipianina-honcharenko, K., et al. (2022). Method of forming the context of advertising and target audience based on associative rules learning. Herald of Khmelnytskyi National University. Technical sciences, 313(5), 279-287. https://doi.org/10.31891/2307-5732-2022-313-5-279-287
12. Lipianina-Honcharenko, K., et al. (2022). An Intelligent Method for Forming the Advertising Content of Higher Education Institutions Based on Semantic Analysis. Communications in Computer and Information Science. Cham, 169-182. https://doi.org/10.1007/978-3-031-14841-5_11