Aspect-based Analysis of Advertising Appeals for Search Engine Advertising
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Abstract (arXiv)
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 that different industries have their own effective A3 and that the identification of the A3 contributes to the estimation of advertising performance.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 6 of its 28 references that are in the map. First posted on arXiv 25 Apr 2022.
Builds on
6 of 28 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
- 2017Deep Interest Network for Click-Through Rate Predictioncited by 541
- 2019Towards Knowledge-Based Personalized Product Description Generation in E-commercecited by 32
- 2014Sequential Click Prediction for Sponsored Search with Recurrent Neural Networkscited by 21
- 2021TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similaritycited by 4
Built on by
Citing papers per year: 2023: 1; 2024: 2; 2025: 2.
5 papers.
- 2023Striking Gold in Advertising: Standardization and Exploration of Ad Text Generationcited by 17
- 2025AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Textscited by 5
- 2024AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertisingcited by 4
- 2025AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Datasetcited by 2
- 2024FaithCAMERA: Construction of a Faithful Dataset for Ad Text Generationcited by 0
- 2025AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset27 May 2025
- 2025AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Texts7 Feb 2025
- 2024FaithCAMERA: Construction of a Faithful Dataset for Ad Text Generation4 Oct 2024
- 2024AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising12 Aug 2024
- 2023Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation21 Sep 2023