Aspect-based Analysis of Advertising Appeals for Search Engine Advertising (2022)

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.

## introduction search engine advertising (sea) displays an ad text that consists of a title and a description that are relevant to search queries in search engines, as illustrated in figure 1 . sea plays an important role in sales promotion and marketing as it allows advertisers to approach users who are interested in specific search queries effectively @xcite . ad creators write an ad text that attracts the attention of users and persuades them to click or act by introducing various aspects of advertising appeals (denoted as a 3 in this paper for short), such as special deals, as shown in figure 1 . however, products and services exhibit unique effective a 3 for different industries. for example, limited offers may be attractive to users in the e-commerce (ec) industry, whereas the quality of products may be more important in the automobile industry. thus, we argue that the suggestion of effective a 3 for various industries can offer assistance to ad creators. therefore, we need to discover the effective aspects. however, although aspect-based text analysis has attracted significant attention in the review analysis for products and services @xcite , it has received less focus in the advertisement field. in this work, to deal with this problem, we defined the a 3 and constructed a dataset of ad texts that are annotated with a 3 in various industries as a first attempt towards assisting ad creators with a 3 . subsequently we developed an aspect detection model to identify different a 3 and performed correlation analysis between a 3 and the click-through rate (ctr), which is used for supporting ad creation, as an advertising performance metric to explore the effective aspects in different industries. furthermore, we investigated the effectiveness of a 3 in ctr prediction as a potential application for ad creation support. through correlation analysis in our experiments, we found that different industries exhibit unique effective a 3 . furthermore, we found that the identification of the a 3 contributes to the ctr prediction. ## aspect detection model we investigate two existing models for aspect detection, i.e., the span-based @xcite and document-based (doc-based) models @xcite . these models receive an ad text as an input and predict aspect labels @xmath0, where x i and y i represent a token of an ad text and a binary label for each aspect label, respectively. as each span may contain multiple aspects, both models perform label prediction in the form of multi-label classification @xcite . k is the number of aspect labels defined in ## ctr prediction with a 3 within the context of ad creation support, the estimation of advertising performance for an ad text (e.g., the ctr) plays a key role in both the improvement and cost efficiency of the ad creation because it helps us understand the user's interest. therefore, we also investigate whether the a 3 contributes to the prediction of the advertising performance. for this task, we input an ad text x consisting of a title and description, an industry type of the ad t (e.g., ec), and keywords k (e.g., tokyo and hotel). we also introduce the predicted aspect labels ŷ (e.g., features) for x as additional features, which were detected by either the span-based or doc-based model. in this case, we use the ctr @xmath2, 1] as the advertising performance (@xmath3. figure 3 presents an overview of the regression model. similarly to recent work @xcite , we design this regression model based on the bert. in the model, we feed the three types of tokens x, t, k into the bert to obtain the vector h [cls] for a [cls] token. subsequently, we input h [cls] and the aspect labels ŷ for the ad text x into the following mlp. thereafter, we obtain the concatenated vector h @xmath4; h aspect ], where ";" is a concatenation operator. the final mlp then predicts a ctr score z from h out as @xmath5. ## experiments we conducted experiments on three tasks: (1) aspect detection for the a 3 , (2) correlation analysis between the a 3 and ctr, and (3) ctr prediction. ## conclusions in this work, we have explored the effective a 3 by means of aspect detection and correlation analysis towards ad creation support with the a 3 . our experimental results demonstrated that each industry exhibits unique effective a 3 and that identification of the a 3 can contributes to ctr prediction. we demonstrate two possible directions for future studies. first, we will investigate whether introducing the effective a 3 in the ad creation process can help ad creators write effective ad texts in realworld applications. second, we will develop an aspect-aware model to automatically generate ad texts to support the ad creation process. for the latter, we will train the model with a dataset that includes pairs of ad texts and their corresponding aspect labels predicted using aspect detection.