Sentiment Recognition in Egocentric Photostreams
This addresses sentiment recognition for lifelogging applications, but it is incremental as it applies existing CNN methods to a new domain.
The paper tackles sentiment analysis in egocentric photostreams by classifying images into positive, neutral, or negative feelings, using global and semantic features from CNNs, with experiments conducted on a custom dataset.
Lifelogging is a process of collecting rich source of information about daily life of people. In this paper, we introduce the problem of sentiment analysis in egocentric events focusing on the moments that compose the images recalling positive, neutral or negative feelings to the observer. We propose a method for the classification of the sentiments in egocentric pictures based on global and semantic image features extracted by Convolutional Neural Networks. We carried out experiments on an egocentric dataset, which we organized in 3 classes on the basis of the sentiment that is recalled to the user (positive, negative or neutral).