  Opinion mining in outdoor images posted by users during different activities
can provide valuable information to better understand urban areas. In this
regard, we propose a framework to classify the sentiment of outdoor images
shared by users on social networks. We compare the performance of
state-of-the-art ConvNet architectures, and one specifically designed for
sentiment analysis. We also evaluate how the merging of deep features and
semantic information derived from the scene attributes can improve
classification and cross-dataset generalization performance. The evaluation
explores a novel dataset, namely OutdoorSent, and other datasets publicly
available. We observe that the incorporation of knowledge about semantic
attributes improves the accuracy of all ConvNet architectures studied. Besides,
we found that exploring only images related to the context of the study,
outdoor in our case, is recommended, i.e., indoor images were not significantly
helpful. Furthermore, we demonstrated the applicability of our results in the
city of Chicago, USA, showing that they can help to improve the knowledge of
subjective characteristics of different areas of the city. For instance,
particular areas of the city tend to concentrate more images of a specific
class of sentiment, which are also correlated with median income, opening up
opportunities in different fields.
