A Blast From the Past: Personalizing Predictions of Video-Induced Emotions using Personal Memories as Context

TL;DR

This paper demonstrates that analyzing personal memories triggered by videos, combined with audiovisual content analysis, improves the accuracy of predicting viewers' emotional responses in personalized settings.

Contribution

It introduces an automatic method to analyze memory descriptions and combines this with audiovisual analysis to enhance emotion prediction accuracy.

Findings

Memory analysis explains more emotional variation than demographics.

Combining memory and audiovisual analysis improves prediction accuracy.

The approach advances personalized affective video analysis.

Abstract

A key challenge in the accurate prediction of viewers' emotional responses to video stimuli in real-world applications is accounting for person- and situation-specific variation. An important contextual influence shaping individuals' subjective experience of a video is the personal memories that it triggers in them. Prior research has found that this memory influence explains more variation in video-induced emotions than other contextual variables commonly used for personalizing predictions, such as viewers' demographics or personality. In this article, we show that (1) automatic analysis of text describing their video-triggered memories can account for variation in viewers' emotional responses, and (2) that combining such an analysis with that of a video's audiovisual content enhances the accuracy of automatic predictions. We discuss the relevance of these findings for improving on state of the art approaches to automated affective video analysis in personalized contexts.