Emotion evoked by an advertisement plays a key role in influencing brand recall and eventual consumer choices. Automatic ad affect recognition has several useful applications. However, the use of content-based feature representations does not give insights into how affect is modulated by aspects such as the ad scene setting, salient object attributes and their interactions. Neither do such approaches inform us on how humans prioritize visual information for ad understanding. Our work addresses these lacunae by decomposing video content into detected objects, coarse scene structure, object statistics and actively attended objects identified via eye-gaze. We measure the importance of each of these information channels by systematically incorporating related information into ad affect prediction models. Contrary to the popular notion that ad affect hinges on the narrative and the clever use of linguistic and social cues, we find that actively attended objects and the coarse scene structure better encode affective information as compared to individual scene objects or conspicuous background elements.
Advertisements are inherently emotional, and have long been shown to modulate user behaviour towards products in response to the emotions they induce @cite_27 @cite_54 @cite_50 . Holbrook and Batra @cite_54 observe correlations between emotions induced by ads, and users' brand attitude. Pham al @cite_50 note that ad-evoked feelings impact users both explicitly and implicitly. Recent work has aimed at exploiting the affective information present in ads for computational advertising, , inserting the most emotionally relevant ads in scene transition points within programs. Shukla al @cite_25 @cite_4 show that improved audiovisual affect recognition can enhance ad recall and improve viewer experience when inserting ads in videos. Products deployed in the industry such as and by RealEyes @cite_19 , which provide second-by-second emotion traces and predict exactly how people react to media content, thereby revealing what works and where the pitfalls lie. These methods exploit emotional content and structure of advertisements to perform precise ad characterization, which underlines the importance of emotions in ads.
@cite_4 @cite_54 @cite_19 @cite_27 @cite_50 @cite_25
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Despite the fact that advertisements (ads) often include strongly emotional content, very little work has been devoted to affect recognition (AR) from ads. This work explicitly compares content-centric and user-centric ad AR methodologies, and evaluates the impact of enhanced AR on computational advertising via a user study. Specifically, we (1) compile an affective ad dataset capable of evoking coherent emotions across users; (2) explore the efficacy of content-centric convolutional neural network (CNN) features for encoding emotions, and show that CNN features outperform low-level emotion descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram (EEG) responses acquired from eleven viewers, and find that EEG signals encode emotional information better than content descriptors; (4) investigate the relationship between objective AR and subjective viewer experience while watching an ad-embedded online video stream based on a study involving 12 users. To our knowledge, this is the first work to (a) expressly compare user vs content-centered AR for ads, and (b) study the relationship between modeling of ad emotions and its impact on a real-life advertising application. This article pursues the emerging interest in emotional aspects of consumer behavior, advocates a broadened view of consumption-related emotions, and focuses on the role of emotions in mediating the effects of advertising. Specifically, it proposes an approach that examines the manner in which intervening emotional reactions mediate the relationship between advertising content and attitudes toward the ad or brand. An illustrative application of this approach demonstrates its usefulness in assessing the role of emotions as mediators of consumer responses to advertising. This paper addresses the reemergence of attention to the role of emotion in advertising. Conceptual clarity requires that emotions be distinguished from other motivational and affective constructs. Having thus narrowed our focus, we present a model of the emotional process and a typology of emotional content. We then address some related managerial issues in advertising strategy and methodological problems in measuring emotions. Finally, the preceding considerations suggest directions for future research on the role of emotion in advertising. It has been observed that ad-evoked feelings exert a positive influence on brand attitudes. To investigate the empirical generalizability of this phenomenon, we analyzed the responses of 1576 consumers to 1070 TV commercials from more than 150 different product categories. The findings suggest five empirical generalizations. First, ad-evoked feelings indeed have a substantial impact on brand evaluations, even under conditions that better approximate real marketplace settings than past studies did. Second, these effects are both direct and indirect, with the indirect effects largely linked to changes in attitude toward the ad. Third, these effects do not depend on the level of involvement associated with the product category. However, fourth, the effects are more pronounced for hedonic products than utilitarian products. Finally, these effects do not depend on whether the products are durables, nondurables, or services, or whether the products are search goods or experience goods.