# Towards Emotion Retrieval in Egocentric PhotoStream

## TL;DR

This paper introduces a method for sentiment analysis of egocentric photo streams, aiming to identify positive, neutral, or negative events to enhance emotion retrieval from wearable camera data.

## Contribution

It presents a novel approach for assigning sentiment to events in egocentric photostreams, advancing emotion recognition in wearable camera data analysis.

## Key findings

- Achieved 75% classification accuracy on test data.
- Demonstrated the feasibility of sentiment recognition in egocentric images.
- Opened new avenues for emotion retrieval in wearable camera research.

## Abstract

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 a deviation of 8%. Our model makes a step forward opening the door to sentiment recognition in egocentric photostreams.