  How can we define visual sentiment when viewers systematically disagree on
their perspectives? This study introduces a novel approach to visual sentiment
analysis by integrating attitudinal differences into visual sentiment
classification. Recognizing that societal divides, such as partisan
differences, heavily influence sentiment labeling, we developed a dataset that
reflects these divides. We then trained a deep learning multi-task multi-class
model to predict visual sentiment from different ideological viewpoints.
Applied to immigration-related images, our approach captures perspectives from
both Democrats and Republicans. By incorporating diverse perspectives into the
labeling and model training process, our strategy addresses the limitation of
label ambiguity and demonstrates improved accuracy in visual sentiment
predictions. Overall, our study advocates for a paradigm shift in decoding
visual sentiment toward creating classifiers that more accurately reflect the
sentiments generated by humans.
