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Can We Categorize Moviegoers on Their Emotions?

Emilie Poirson
LUNAM Universitå, Ecole Centrale Nantes, IRCCyN UMR CNRS 6597 (Institut de Recherche en Communications et Cybernåtique de Nantes) Nantes, France

Catherine da Cunha
LUNAM Universitå, Ecole Centrale Nantes, IRCCyN UMR CNRS 6597 (Institut de Recherche en Communications et Cybernåtique de Nantes) Nantes, France

Jean-François Petiot
LUNAM Universitå, Ecole Centrale Nantes, IRCCyN UMR CNRS 6597 (Institut de Recherche en Communications et Cybernåtique de Nantes) Nantes, France

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Ingår i: KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13

Linköping Electronic Conference Proceedings 100:101, s. 1219-1216

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Publicerad: 2014-06-11

ISBN: 978-91-7519-276-5

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

The choice of a product is based on the performances but also on the emotions it caused; particularly in cultural products. We hypothesize that customer preferences are partly dependent on the emotions aroused by the use. For each pair user/product type there exists a function linking emotions and preference. The design of online recommendation systems; such as those used in e-commerce; is a real challenge. This requires understanding the customer’s need in order to recommend the right products for them; i.e. those that are likely to be appreciated. The current recommendation systems rely on similarities between customers based on products purchased (or assessed). The best way to advise an amateur is then to determine its emotional neighborhood; and recommend the products liked by its neighbors. To validate this hypothesis a full-scale study was conducted on a given product: movie. After determination of a list of emotions adapted to movies; data were collected through an online survey. The data processing (more than 6500 evaluations) has several objectives: 1 . Identify; for each client; relationships between emotions and overall assessment; 2 . Identify groups of customers with similar overall assessments; 3 . Identify groups of customers with similar relationships.

Nyckelord

User; Emotions; Preference; Neighborhood

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