Peinan Zhang


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Aspect-based Analysis of Advertising Appeals for Search Engine Advertising
Soichiro Murakami | Peinan Zhang | Sho Hoshino | Hidetaka Kamigaito | Hiroya Takamura | Manabu Okumura
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track

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.


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An Empirical Study of Generating Texts for Search Engine Advertising
Hidetaka Kamigaito | Peinan Zhang | Hiroya Takamura | Manabu Okumura
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Although there are many studies on neural language generation (NLG), few trials are put into the real world, especially in the advertising domain. Generating ads with NLG models can help copywriters in their creation. However, few studies have adequately evaluated the effect of generated ads with actual serving included because it requires a large amount of training data and a particular environment. In this paper, we demonstrate a practical use case of generating ad-text with an NLG model. Specially, we show how to improve the ads’ impact, deploy models to a product, and evaluate the generated ads.

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FAST: Fast Annotation tool for SmarT devices
Shunyo Kawamoto | Yu Sawai | Kohei Wakimoto | Peinan Zhang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Working with a wide range of annotators with the same attributes is crucial, as in real-world applications. Although such application cases often use crowd-sourcing mechanisms to gather a variety of annotators, most real-world users use mobile devices. In this paper, we propose “FAST,” an annotation tool for application tasks that focuses on the user experience of mobile devices, which has not yet been focused on thus far. We designed FAST as a web application for use on any device with a flexible interface that can be customized to fit various tasks. In our experiments, we conducted crowd-sourced annotation for a sentiment analysis task with several annotators and evaluated annotation metrics such as speed, quality, and ease of use from the tool’s logs and user surveys. Based on the results of our experiments, we conclude that our system can annotate faster than existing methods while maintaining the annotation quality.


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Japanese Sentiment Classification with Stacked Denoising Auto-Encoder using Distributed Word Representation
Peinan Zhang | Mamoru Komachi
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation