@inproceedings{murakami-etal-2022-aspect,
title = "Aspect-based Analysis of Advertising Appeals for Search Engine Advertising",
author = "Murakami, Soichiro and
Zhang, Peinan and
Hoshino, Sho and
Kamigaito, Hidetaka and
Takamura, Hiroya and
Okumura, Manabu",
editor = "Loukina, Anastassia and
Gangadharaiah, Rashmi and
Min, Bonan",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-industry.9",
doi = "10.18653/v1/2022.naacl-industry.9",
pages = "69--78",
abstract = "Writing an ad text that attracts people and persuades them to click or act is essential for the success of search engine advertising. Therefore, ad creators must consider various aspects of advertising appeals (A$^3$) such as the price, product features, and quality. However, products and services exhibit unique effective A$^3$ for different industries. In this work, we focus on exploring the effective A$^3$ for different industries with the aim of assisting the ad creation process. To this end, we created a dataset of advertising appeals and used an existing model that detects various aspects for ad texts. Our experiments demonstrated {\%}through correlation analysis that different industries have their own effective A$^3$ and that the identification of the A$^3$ contributes to the estimation of advertising performance.",
}
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<abstract>Writing an ad text that attracts people and persuades them to click or act is essential for the success of search engine advertising. Therefore, ad creators must consider various aspects of advertising appeals (A³) such as the price, product features, and quality. However, products and services exhibit unique effective A³ for different industries. In this work, we focus on exploring the effective A³ for different industries with the aim of assisting the ad creation process. To this end, we created a dataset of advertising appeals and used an existing model that detects various aspects for ad texts. Our experiments demonstrated %through correlation analysis that different industries have their own effective A³ and that the identification of the A³ contributes to the estimation of advertising performance.</abstract>
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%0 Conference Proceedings
%T Aspect-based Analysis of Advertising Appeals for Search Engine Advertising
%A Murakami, Soichiro
%A Zhang, Peinan
%A Hoshino, Sho
%A Kamigaito, Hidetaka
%A Takamura, Hiroya
%A Okumura, Manabu
%Y Loukina, Anastassia
%Y Gangadharaiah, Rashmi
%Y Min, Bonan
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track
%D 2022
%8 July
%I Association for Computational Linguistics
%C Hybrid: Seattle, Washington + Online
%F murakami-etal-2022-aspect
%X Writing an ad text that attracts people and persuades them to click or act is essential for the success of search engine advertising. Therefore, ad creators must consider various aspects of advertising appeals (A³) such as the price, product features, and quality. However, products and services exhibit unique effective A³ for different industries. In this work, we focus on exploring the effective A³ for different industries with the aim of assisting the ad creation process. To this end, we created a dataset of advertising appeals and used an existing model that detects various aspects for ad texts. Our experiments demonstrated %through correlation analysis that different industries have their own effective A³ and that the identification of the A³ contributes to the estimation of advertising performance.
%R 10.18653/v1/2022.naacl-industry.9
%U https://aclanthology.org/2022.naacl-industry.9
%U https://doi.org/10.18653/v1/2022.naacl-industry.9
%P 69-78
Markdown (Informal)
[Aspect-based Analysis of Advertising Appeals for Search Engine Advertising](https://aclanthology.org/2022.naacl-industry.9) (Murakami et al., NAACL 2022)
ACL
- Soichiro Murakami, Peinan Zhang, Sho Hoshino, Hidetaka Kamigaito, Hiroya Takamura, and Manabu Okumura. 2022. Aspect-based Analysis of Advertising Appeals for Search Engine Advertising. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, pages 69–78, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.