Details
| Original language | English |
|---|---|
| Title of host publication | HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media |
| Pages | 185-194 |
| Number of pages | 10 |
| ISBN (electronic) | 9781450333955 |
| Publication status | Published - 24 Aug 2015 |
| Event | 26th ACM Conference on Hypertext and Social Media, HT 2015 - Guzelyurt, Cyprus Duration: 1 Sept 2015 → 4 Sept 2015 |
Publication series
| Name | HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media |
|---|
Abstract
Emotion is fundamental to human experience and impacts our daily activities and decision-making processes where, e.g., the affective state of a user influences whether or not she decides to consume a recommended item - movie, book, product or service. However, information retrieval and recommendation tasks have largely ignored emotion as a source of user context, in part because emotion is difficult to measure and easy to misunderstand. In this paper we explore the role of emotions in short films and propose an approach that automatically extracts affective context from user comments associated to short films available in YouTube, as an alternative to explicit human annotations. We go beyond the traditional polarity detection (i.e., positive/negative), and extract for each film four opposing pairs of primary emotions: joy-sadness, anger-fear, trust-disgust, and anticipation-surprise. Finally, in our empirical evaluation, we show how the affective context extracted automatically can be leveraged for emotion-aware film recommendation.
Keywords
- Computational social science, Sentiment analysis, Social media analytics, YouTube
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Human-Computer Interaction
Cite this
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HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media. 2015. p. 185-194 (HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Mining Affective Context in Short Films for Emotion-Aware Recommendation
AU - Orellana-Rodriguez, Claudia
AU - Diaz-Aviles, Ernesto
AU - Nejdl, Wolfgang
N1 - Funding information: This work was supported in part by Science Foundation Ireland - Grant Number: 12/RC/2289.
PY - 2015/8/24
Y1 - 2015/8/24
N2 - Emotion is fundamental to human experience and impacts our daily activities and decision-making processes where, e.g., the affective state of a user influences whether or not she decides to consume a recommended item - movie, book, product or service. However, information retrieval and recommendation tasks have largely ignored emotion as a source of user context, in part because emotion is difficult to measure and easy to misunderstand. In this paper we explore the role of emotions in short films and propose an approach that automatically extracts affective context from user comments associated to short films available in YouTube, as an alternative to explicit human annotations. We go beyond the traditional polarity detection (i.e., positive/negative), and extract for each film four opposing pairs of primary emotions: joy-sadness, anger-fear, trust-disgust, and anticipation-surprise. Finally, in our empirical evaluation, we show how the affective context extracted automatically can be leveraged for emotion-aware film recommendation.
AB - Emotion is fundamental to human experience and impacts our daily activities and decision-making processes where, e.g., the affective state of a user influences whether or not she decides to consume a recommended item - movie, book, product or service. However, information retrieval and recommendation tasks have largely ignored emotion as a source of user context, in part because emotion is difficult to measure and easy to misunderstand. In this paper we explore the role of emotions in short films and propose an approach that automatically extracts affective context from user comments associated to short films available in YouTube, as an alternative to explicit human annotations. We go beyond the traditional polarity detection (i.e., positive/negative), and extract for each film four opposing pairs of primary emotions: joy-sadness, anger-fear, trust-disgust, and anticipation-surprise. Finally, in our empirical evaluation, we show how the affective context extracted automatically can be leveraged for emotion-aware film recommendation.
KW - Computational social science
KW - Sentiment analysis
KW - Social media analytics
KW - YouTube
UR - http://www.scopus.com/inward/record.url?scp=84957036521&partnerID=8YFLogxK
U2 - 10.1145/2700171.2791042
DO - 10.1145/2700171.2791042
M3 - Conference contribution
AN - SCOPUS:84957036521
T3 - HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media
SP - 185
EP - 194
BT - HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media
T2 - 26th ACM Conference on Hypertext and Social Media, HT 2015
Y2 - 1 September 2015 through 4 September 2015
ER -