The availability and use of egocentric data are rapidly increasing due to the growing use of wearable cameras. Our aim is to study the effect (positive, neutral or negative) of egocentric images or events on an observer. Given egocentric photostreams capturing the wearer’s days, we propose a method that aims to assign sentiment to events extracted from egocentric photostreams. Such moments can be candidates to retrieve according to their possibility of representing a positive experience for the camera’s wearer. The proposed approach obtained a classification accuracy of 75% on the test set, with deviation of 8%. Our model makes a step forward opening the door to sentiment recognition in egocentric photostreams.
CITATION STYLE
Talavera, E., Radeva, P., & Petkov, N. (2018). Towards Egocentric Sentiment Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10672 LNCS, pp. 297–305). Springer Verlag. https://doi.org/10.1007/978-3-319-74727-9_35
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