OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features

Opinion mining in outdoor images posted by users during day-to-day or leisure activities, for example, can provide valuable information to better understand urban areas. In this work, 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, namely, VGG-16, Resnet50, and InceptionV3, as well as one specifically designed for sentiment analysis. The combination of such classifiers, a strategy known as ensemble, is also considered. We also use different experimental setups to evaluate how the merging of deep features and semantic information derived from the scene attributes can improve classification performance. The evaluation explores a novel dataset, namely OutdoorSent, of geolocalized urban outdoor images extracted from Instagram related to three sentiment polarities (positive, negative, and neutral), as well as another dataset publicly available (DeepSent). We observe that the incorporation of knowledge related to semantics features tend to improve the accuracy of low-complex ConvNet architectures. Furthermore, we also demonstrated the applicability of our results in the city of Chicago, United States, showing that they can help to understand the 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. The ConvNet architectures, trained models, and the proposed outdoor image dataset will be publicly available at http://dainf.ct.utfpr.edu.br/outdoorsent.

OutdoorSent: Can Semantic Features Help Deep Learning in Sentiment Analysis of Outdoor Images?

Opinion mining in outdoor images posted by users during day-to-day or leisure activities, for example, can provide valuable information to better understand urban areas. In this work, 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, namely, VGG-16, Resnet50, and InceptionV3, as well as one specifically designed for sentiment analysis. The combination of such classifiers, a strategy known as ensemble, is also considered. We also use different experimental setups to evaluate how the merging of deep features and semantic information derived from the scene attributes can improve classification performance. The evaluation explores a novel dataset, namely OutdoorSent, of geolocalized urban outdoor images extracted from Instagram related to three sentiment polarities (positive, negative, and neutral), as well as another dataset publicly available (DeepSent). We observe that the incorporation of knowledge related to semantics features tend to improve the accuracy of low-complex ConvNet architectures. Furthermore, we also demonstrated the applicability of our results in the city of Chicago, United States, showing that they can help to understand the 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. The ConvNet architectures, trained models, and the proposed outdoor image dataset will be publicly available at http://dainf.ct.utfpr.edu.br/outdoorsent.