Mining Affective Context in Short Films for Emotion-Aware Recommendation

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Title: Mining Affective Context in Short Films for Emotion-Aware Recommendation
Authors: Orellana-Rodriguez, Claudia
Díaz-Aviles, Ernesto
Nejdl, Wolfgang
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Date: 4-Sep-2015
Abstract: Emotion is fundamental to human experience and impactsour daily activities and decision-making processes where,e.g., the affective state of a user influences whether or notshe decides to consume a recommended item movie, book,product or service. However, information retrieval and recommendationtasks have largely ignored emotion as a sourceof user context, in part because emotion is difficult to measureand easy to misunderstand. In this paper we explore therole of emotions in short films and propose an approach thatautomatically extracts affective context from user commentsassociated to short films available in YouTube, as an alternativeto explicit human annotations. We go beyond the traditionalpolarity detection (i.e., positive/negative), and extractfor each film four opposing pairs of primary emotions:joysadness, angerfear, trustdisgust, and anticipationsurprise. Finally, in our empirical evaluation, we show howthe affective context extracted automatically can be leveragedfor emotion-aware film recommendation.
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2015 the Authors
Keywords: Machine learningStatisticsComputational social scienceSentiment analysisSocial media analyticsYouTube
DOI: 10.1145/2700171.2791042
Language: en
Status of Item: Peer reviewed
Conference Details: Proceedings of the 26th ACM Conference on Hypertext and Social Media, Middle East Technical University Northern Cyprus Campus, Cyprus, 1-4 September 2015
Appears in Collections:Insight Research Collection

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