This advert makes me cry: Disclosure of emotional response to advertisement on Facebook

As social media is transforming how consumers interact with brands and how brand-related content is consumed, this paper aims to investigate if and how Facebook users express their emotions towards advertisements of brand share on the site. Seven hundred and three comments about the Lloyds 250th Anniversary advertisement on Facebook were analysed as positive, negative or neutral attitude towards the advert. Facebook users found the advertisement emotionally appealing and voluntarily report their emotion of love, pride and in some cases anger. The presence of an iconic image like the black horse and the cover music was found to be emotionally appealing. The background music as well aroused positive emotions and engaging. This study introduces the possibility of analysing Facebook comments on brand content to understand consumers’ emotional responses and attitudes to the brand. Managers can explore these opportunities to identify what consumers find interesting in advertisements and how best to develop their creative strategies. It also offers the opportunity to allocate resources better to engage consumers with creative advertisement. Unlike interviews or surveys, this is a pioneering study on measuring emotional responses to advertisement through users’ self-report on social media

Exploiting Sparsity and Co-occurrence Structure for Action Unit Recognition

Abstract-We present a novel Bayesian framework for facial action unit recognition. The first key observation behind this work is sparsity: out of possible 45 (and more) facial action units, only very few are active at any moment. The second is the strong statistical co-occurrence structure: most facial expressions are made by common combinations of facial action units, so knowing the presence of one can act as a strong prior for inferring the presence of others. We developed a novel Bayesian graphical model that encodes these two natural aspects of facial action units via compressed sensing and group-wise sparsity inducing priors. One crucial aspect of our approach is the allowance of overlapping group structures, which proves useful in dealing with action units that occur frequently across multiple groups. We derive an efficient inference scheme and show how such sparsity and co-occurrence can be automatically learned from data. Experiments on three standard benchmark datasets show superiority over the state-of-the-art