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_28: A picture is worth one thousand words, but what words should be used to describe the sentiment and emotions conveyed in the increasingly popular social multimedia? We demonstrate a novel system which combines sound structures from psychology and the folksonomy extracted from social multimedia to develop a large visual sentiment ontology consisting of 1,200 concepts and associated classifiers called SentiBank. Each concept, defined as an Adjective Noun Pair (ANP), is made of an adjective strongly indicating emotions and a noun corresponding to objects or scenes that have a reasonable prospect of automatic detection. We believe such large-scale visual classifiers offer a powerful mid-level semantic representation enabling high-level sentiment analysis of social multimedia. We demonstrate novel applications made possible by SentiBank including live sentiment prediction of social media and visualization of visual content in a rich intuitive semantic space.
  @cite_35: We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key contribution is two-fold: first, we present a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP). Second, we propose SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image. The VSO and SentiBank are distinct from existing work and will open a gate towards various applications enabled by automatic sentiment analysis. Experiments on detecting sentiment of image tweets demonstrate significant improvement in detection accuracy when comparing the proposed SentiBank based predictors with the text-based approaches. The effort also leads to a large publicly available resource consisting of a visual sentiment ontology, a large detector library, and the training testing benchmark for visual sentiment analysis.
  @cite_9: Visual sentiment analysis is getting increasing attention because of the rapidly growing amount of images in online social interactions and several emerging applications such as online propaganda and advertisement. Recent studies have shown promising progress in analyzing visual affect concepts intended by the media content publisher. In contrast, this paper focuses on predicting what viewer affect concepts will be triggered when the image is perceived by the viewers. For example, given an image tagged with "yummy food," the viewers are likely to comment "delicious" and "hungry," which we refer to as viewer affect concepts (VAC) in this paper. To the best of our knowledge, this is the first work explicitly distinguishing intended publisher affect concepts and induced viewer affect concepts associated with social visual content, and aiming at understanding their correlations. We present around 400 VACs automatically mined from million-scale real user comments associated with images in social media. Furthermore, we propose an automatic visual based approach to predict VACs by first detecting publisher affect concepts in image content and then applying statistical correlations between such publisher affect concepts and the VACs. We demonstrate major benefits of the proposed methods in several real-world tasks - recommending images to invoke certain target VACs among viewers, increasing the accuracy of predicting VACs by 20.1 and finally developing a social assistant tool that may suggest plausible, content-specific and desirable comments when users view new images.
  @cite_32: Animated GIFs are everywhere on the Web. Our work focuses on the computational prediction of emotions perceived by viewers after they are shown animated GIF images. We evaluate our results on a dataset of over 3,800 animated GIFs gathered from MIT's GIFGIF platform, each with scores for 17 discrete emotions aggregated from over 2.5M user annotations - the first computational evaluation of its kind for content-based prediction on animated GIFs to our knowledge. In addition, we advocate a conceptual paradigm in emotion prediction that shows delineating distinct types of emotion is important and is useful to be concrete about the emotion target. One of our objectives is to systematically compare different types of content features for emotion prediction, including low-level, aesthetics, semantic and face features. We also formulate a multi-task regression problem to evaluate whether viewer perceived emotion prediction can benefit from jointly learning across emotion classes compared to disjoint, independent learning.
summary: It is important to distinguish our work from that of on VSO @cite_35 and its associated detector bank, SentiBank @cite_28 . Their mid-level representation approach has recently proven effective in a wide range of applications in emotion prediction @cite_28 @cite_32 , social media commenting @cite_9 , etc. However, in addition to lack of multilingual support, there are several technical challenges with VSO @cite_28 @cite_35 that we seek to improve on via (1) detection of adjectives and nouns with language-specific part-of-speech taggers, as opposed to a fixed list of adjectives and nouns, (2) automatic discovery of adjective-noun pairs correlated with emotions, as opposed to constructed'' pairs from top frequent adjectives and nouns, and (3) stronger selection criterion based on image tag frequency, linguistic and semantic filters and crowdsource validation.. Our proposed MVSO discovery method can be easily extended to any language, while achieving greater coverage and diversity than VSO.