text: Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic structures called adjective-noun pairs, originally introduced by (2013), but in a multilingual context. We propose a new language-dependent method for automatic discovery of these adjective-noun constructs. We show how this pipeline can be applied on a social multimedia platform for the creation of a large-scale multilingual visual sentiment concept ontology (MVSO). Unlike the flat structure in (2013), our unified ontology is organized hierarchically by multilingual clusters of visually detectable nouns and subclusters of emotionally biased versions of these nouns. In addition, we present an image-based prediction task to show how generalizable language-specific models are in a multilingual context. A new, publicly available dataset of >15.6K sentiment-biased visual concepts across 12 languages with language-specific detector banks, >7.36M images and their metadata is also released.
  @cite_38: Research on culture and emotion has moved beyond the once central nature–nurture. Evidence suggests that there are universal constituents of emotions – the ‘emotional potential’ – rather than universal emotions. Furthermore, the emotional constituents are assembled in culture-specific ways that are meaningful and predictable, resulting in systematic cultural differences in ‘emotional practices’ (i.e., people's actual emotional lives). Whereas emotions are not universal, the underlying rule of emotional life is: Culturally adaptive emotions are more prevalent and intense. After reviewing empirical evidence for each of these claims, we outline some of the future directions for research on culture and emotion.
  @cite_4: The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3, 800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.
  @cite_8: 
  @cite_6: We propose a principled method for designing high level features forphoto quality assessment. Our resulting system can classify between high quality professional photos and low quality snapshots. Instead of using the bag of low-level features approach, we first determine the perceptual factors that distinguish between professional photos and snapshots. Then, we design high level semantic features to measure the perceptual differences. We test our features on a large and diverse dataset and our system is able to achieve a classification rate of 72 on this difficult task. Since our system is able to achieve a precision of over 90 in low recall scenarios, we show excellent results in a web image search application.
  @cite_39: ABSTRACT How is visual information linked to aesthetic experience, and what factors determine whether an individual finds a particular visual experience pleasing? We have previously shown that individuals aesthetic responses are not determined by objective image features but are instead a function of internal, subjective factors that are shaped by a viewers personal experience. Ye t for many classes of stimuli, culturally shared semantic associations give rise to similar aesthetic taste across people. In this paper, we investigated factors that govern whether a set of observers will agree in which images are preferred, or will instead exhibit more per sonalized aesthetic preferences. In a series of experiments, observers were asked to make aesthetic judgments for different categories of visual stimuli that are commonly evaluated in an aesthetic manner (faces, natural landscapes, architecture or artwork). By measuring agreement across observers, this method was able to reveal instances of highly individualistic preferences. We found that observers showed high agreement on their preferences for images of faces and landscapes, but much lower agreement for images of artwork and architecture. In addition, we found higher agreement for he terosexual males making judgments of beautiful female faces than of beautiful male faces. These results suggest that preferences for stimulus categories that carry evolutionary significance (landscapes and faces) come to rely on similar information across individuals, whereas preferences for artifacts of human culture such as architecture and artwork, which have fewer basic-level category distinctions and reduced behavioral relevance, rely on a more personalized set of attributes. Keywords: Aesthetics, individual differences, visual preference, artwork, landscapes, faces, architecture
  @cite_19: Hundreds of thousands of photographs are uploaded to the internet every minute through various social networking and photo sharing platforms. While some images get millions of views, others are completely ignored. Even from the same users, different photographs receive different number of views. This begs the question: What makes a photograph popular? Can we predict the number of views a photograph will receive even before it is uploaded? These are some of the questions we address in this work. We investigate two key components of an image that affect its popularity, namely the image content and social context. Using a dataset of about 2.3 million images from Flickr, we demonstrate that we can reliably predict the normalized view count of images with a rank correlation of 0.81 using both image content and social cues. In this paper, we show the importance of image cues such as color, gradients, deep learning features and the set of objects present, as well as the importance of various social cues such as number of friends or number of photos uploaded that lead to high or low popularity of images.
  @cite_40: Cross-cultural research on facial expression and the developments of methods to measure facial expression are briefly summarized. What has been learned about emotion from this work on the face is then elucidated. Four questions about facial expression and emotion are discussed. What information does an expression typically convey? Can there be emotion without facial expression? Can there be a facial expression of emotion without emotion? How do individuals differ in their facial expressions of emotion?
  @cite_2: People in different cultures have strikingly different construals of the self, of others, and of the interdependence of the 2. These construals can influence, and in many cases determine, the very nature of individual experience, including cognition, emotion, and motivation. Many Asian cultures have distinct conceptions of individuality that insist on the fundamental relatedness of individuals to each other. The emphasis is on attending to others, fitting in, and harmonious interdependence with them. American culture neither assumes nor values such an overt connectedness among individuals. In contrast, individuals seek to maintain their independence from others by attending to the self and by discovering and expressing their unique inner attributes. As proposed herein, these construals are even more powerful than previously imagined. Theories of the self from both psychology and anthropology are integrated to define in detail the difference between a construal of the self as independent and a construal of the self as interdependent. Each of these divergent construals should have a set of specific consequences for cognition, emotion, and motivation; these consequences are proposed and relevant empirical literature is reviewed. Focusing on differences in self-construals enables apparently inconsistent empirical findings to be reconciled, and raises questions about what have been thought to be culture-free aspects of cognition, emotion, and motivation.
  @cite_5: 
  @cite_15: We investigate human interest in photos. Based on our own and others' psychophysical experiments, we identify various cues for "interestingness", namely aesthetics, unusualness and general preferences. For the ranking of retrieved images, interestingness shows to be more appropriate than cues proposed earlier. Interestingness is correlated with what people believe they will remember. This is opposed to actual memorability, which is uncorrelated to both. We introduce a set of features computationally capturing the three main aspects of visual interestingness and build an interestingness predictor from them. Its performance is shown on three datasets with varying context, reflecting the prior knowledge of the viewers.
summary: We address the general challenge of , aiming at both recognition and analysis of sentiment and emotions in visual data, but from a multilingual and cross-cultural perspective. Our work is closely related to Multimedia and Vision research that focus on visual aesthetics @cite_6 , interestingness @cite_15 , popularity @cite_19 , and creativity @cite_4 . Our work also relates to research in Cognitive and Social Psychology, especially emotion and culture research @cite_40 @cite_38 @cite_8 , but also neuroaesthetics @cite_39 , visual preference @cite_39 @cite_5 , and social interaction @cite_2 .