# Aspect-based Analysis of Advertising Appeals for Search Engine Advertising

arXiv: https://arxiv.org/abs/2204.11445 · HTML: https://mlsanity.com/paper/2204.11445/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Towards Knowledge-Based Personalized Product Description Generation in E-commerce](https://mlsanity.com/paper/1903.12457/) (2019)
- [Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks](https://mlsanity.com/paper/1404.5772/) (2014)
- [TSI: an Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity](https://mlsanity.com/paper/2108.08226/) (2021)

## Built on by (most cited first, 5 of 5)

- [Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation](https://mlsanity.com/paper/2309.12030/) (2023)
- [AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features toward Generating Attractive Ad Texts](https://mlsanity.com/paper/2502.04674/) (2025)
- [AdTEC: A Unified Benchmark for Evaluating Text Quality in Search Engine Advertising](https://mlsanity.com/paper/2408.05906/) (2024)
- [AdParaphrase v2.0: Generating Attractive Ad Texts Using a Preference-Annotated Paraphrase Dataset](https://mlsanity.com/paper/2505.20826/) (2025)
- [FaithCAMERA: Construction of a Faithful Dataset for Ad Text Generation](https://mlsanity.com/paper/2410.03839/) (2024)

## 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.