929 research outputs found
Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology
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 Borth
et al. (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 Borth et al. (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.Comment: 11 pages, to appear at ACM MM'1
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Acoustic and Prosodic Correlates of Social Behavior
We describe acoustic/prosodic and lexical correlates of social variables annotated on a large corpus of task-oriented spontaneous speech. We employ Amazon Mechanical Turk to label the corpus with a large number of social behaviors, examining results of three of these here. We find significant differences between male and female speakers for perceptions of attempts to be liked, likeability, speech planning, that also differ depending upon the gender of their conversational partners
MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction
Moral rhetoric plays a fundamental role in how we perceive and interpret the
information we receive, greatly influencing our decision-making process.
Especially when it comes to controversial social and political issues, our
opinions and attitudes are hardly ever based on evidence alone. The Moral
Foundations Dictionary (MFD) was developed to operationalize moral values in
the text. In this study, we present MoralStrength, a lexicon of approximately
1,000 lemmas, obtained as an extension of the Moral Foundations Dictionary,
based on WordNet synsets. Moreover, for each lemma it provides with a
crowdsourced numeric assessment of Moral Valence, indicating the strength with
which a lemma is expressing the specific value. We evaluated the predictive
potentials of this moral lexicon, defining three utilization approaches of
increased complexity, ranging from lemmas' statistical properties to a deep
learning approach of word embeddings based on semantic similarity. Logistic
regression models trained on the features extracted from MoralStrength,
significantly outperformed the current state-of-the-art, reaching an F1-score
of 87.6% over the previous 62.4% (p-value<0.01), and an average F1-Score of
86.25% over six different datasets. Such findings pave the way for further
research, allowing for an in-depth understanding of moral narratives in text
for a wide range of social issues
The Use of Online Panel Data in Management Research: A Review and Recommendations
Management scholars have long depended on convenience samples to conduct research involving human participants. However, the past decade has seen an emergence of a new convenience sample: online panels and online panel participants. The data these participants provide—online panel data (OPD)—has been embraced by many management scholars owing to the numerous benefits it provides over “traditional” convenience samples. Despite those advantages, OPD has not been warmly received by all. Currently, there is a divide in the field over the appropriateness of OPD in management scholarship. Our review takes aim at the divide with the goal of providing a common understanding of OPD and its utility and providing recommendations regarding when and how to use OPD and how and where to publish it. To accomplish these goals, we inventoried and reviewed OPD use across 13 management journals spanning 2006 to 2017. Our search resulted in 804 OPD-based studies across 439 articles. Notably, our search also identified 26 online panel platforms (“brokers”) used to connect researchers with online panel participants. Importantly, we offer specific guidance to authors, reviewers, and editors, having implications for both micro and macro management scholars
Crowdsourced intuitive visual design feedback
For many people images are a medium preferable to text and yet, with the exception of
star ratings, most formats for conventional computer mediated feedback focus on text.
This thesis develops a new method of crowd feedback for designers based on images.
Visual summaries are generated from a crowd’s feedback images chosen in response to
a design. The summaries provide the designer with impressionistic and inspiring visual
feedback. The thesis sets out the motivation for this new method, describes the
development of perceptually organised image sets and a summarisation algorithm to
implement it. Evaluation studies are reported which, through a mixed methods
approach, provide evidence of the validity and potential of the new image-based
feedback method.
It is concluded that the visual feedback method would be more appealing than text for
that section of the population who may be of a visual cognitive style. Indeed the
evaluation studies are evidence that such users believe images are as good as text when
communicating their emotional reaction about a design. Designer participants reported
being inspired by the visual feedback where, comparably, they were not inspired by
text. They also reported that the feedback can represent the perceived mood in their
designs, and that they would be enthusiastic users of a service offering this new form of
visual design feedback
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