Paper in IJCNN (2017) on “Towards Using Visual Attributes to Infer Image Sentiment Of Social Events”


Unaiza Ahsan, Munmun De Choudhury, Irfan Essa

Towards Using Visual Attributes to Infer Image Sentiment Of Social Events Proceedings Article

In: Proceedings of The International Joint Conference on Neural Networks, International Neural Network Society, Anchorage, Alaska, US, 2017.

Abstract | Links | BibTeX | Tags: computational journalism, computer vision, IJNN, machine learning


Widespread and pervasive adoption of smartphones has led to the instant sharing of photographs that capture events ranging from mundane to life-altering happenings. We propose to capture sentiment information of such social event images leveraging their visual content. Our method extracts an intermediate visual representation of social event images based on the visual attributes that occur in the images going beyond
sentiment-specific attributes. We map the top predicted attributes to sentiments and extract the dominant emotion associated with a picture of a social event. Unlike recent approaches, our method generalizes to various social events and unseen events, which are not available at training time. We demonstrate the effectiveness of our approach on a challenging social event image dataset and our method outperforms state-of-the-art approaches for classifying complex event images into sentiments.

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