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 publicly available datasets. 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 United States city of Chicago, Illinois, 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.

中文翻译：

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户外发送

用户在不同活动期间发布的户外图像中的意见挖掘可以为更好地了解城市地区提供有价值的信息。在这方面，我们提出了一个框架来对用户在社交网络上分享的户外图像的情绪进行分类。我们比较了最先进的 ConvNet 架构和专门为情感分析设计的架构的性能。我们还评估了融合来自场景属性的深层特征和语义信息如何提高分类和跨数据集泛化性能。该评估探索了一个新的数据集，即 OutdoorSent，以及其他公开可用的数据集。我们观察到，关于语义属性的知识的结合提高了所研究的所有 ConvNet 架构的准确性。除了，我们发现建议只探索与研究背景相关的图像——在我们的例子中是室外的——即，室内图像没有显着帮助。此外，我们在美国伊利诺伊州芝加哥市证明了我们的结果的适用性，表明它们可以帮助提高对该城市不同地区主观特征的认识。例如，城市的特定区域倾向于集中更多特定类别情绪的图像，这些图像也与收入中位数相关，从而在不同领域开辟了机会。表明它们可以帮助提高对城市不同区域主观特征的认识。例如，城市的特定区域倾向于集中更多特定类别情绪的图像，这些图像也与收入中位数相关，从而在不同领域开辟了机会。表明它们可以帮助提高对城市不同区域主观特征的认识。例如，城市的特定区域倾向于集中更多特定类别情绪的图像，这些图像也与收入中位数相关，从而在不同领域开辟了机会。