Mining affective context in short films for emotion-aware recommendation

HT 2015 - Proceedings of the 26th ACM Conference on Hypertext and Social Media

Volumn , Issue , 2015, Pages 185-194

  Orellana Rodriguez, Claudia ^a   Diaz Aviles, Ernesto ^b   Nejdl, Wolfgang ^c  

^a UNIVERSITY COLLEGE DUBLIN   (Ireland)

^b IBM RESEARCH   (Ireland)

^c L3S RESEARCH CENTER   (Germany)

Author keywords

Computational social science; Sentiment analysis; Social media analytics; YouTube

Indexed keywords

DECISION MAKING; HYPERTEXT SYSTEMS; SOCIAL NETWORKING (ONLINE);

AFFECTIVE CONTEXTS; COMPUTATIONAL SOCIAL SCIENCE; DECISION MAKING PROCESS; EMPIRICAL EVALUATIONS; POSITIVE/NEGATIVE; SENTIMENT ANALYSIS; SOCIAL MEDIA ANALYTICS; YOUTUBE;

MOTION PICTURES;

EID: 84957036521     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2700171.2791042     Document Type: Conference Paper

References (38)

1. About Tropfest. http://tropfest.com/about/. Accessed: 2015-04.
2. Academy of Motion Picture Arts (AMPAS). http://www.oscars.org/oscars/rules-eligibility. Accessed: 2015-03.
3. Amazon Mechanical Turk. https://www.mturk.com/mturk/welcome. Accessed: 2015-04.
4. MorphAdorner. http://morphadorner.northwestern.edu. Accessed: 2015-03.
5. Tropfest YouTube Channel. https://www.youtube.com/user/tropfest. Accessed: 2015-03.
6. Your Film Festival. https://www.youtube.com/user/yourfilmfestival. Accessed: 2015-03.
7. YouTube API. http://developers.google.com/youtube/v3/. Accessed: 2015-04.
8. Alias-i. LingPipe 4.1.0. http://alias-i.com/lingpipe, 2008. Accessed: 2015-03.
9. G. Adomavicius, B. Mobasher, F. Ricci, and A. Tuzhilin. Context-aware recommender systems. AI Magazine, 32(3), 2011.
10. G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 217-253. Springer US, 2011.
11. R. A. Baeza-Yates and B. A. Ribeiro-Neto. Modern Information Retrieval - the concepts and technology behind search, Second edition. Pearson Education Ltd., Harlow, England, 2011.
12. S. Balakrishnan and S. Chopra. Collaborative ranking. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM'12, pages 143-152, New York, NY, USA, 2012. ACM.
13. C. J. Burges. From ranknet to lambdarank to lambdamart: An overview. Technical Report MSR-TR-2010-82, Microsoft Research, June 2010.
14. C. J. C. Burges, K. M. Svore, P. N. Bennett, A. Pastusiak, and Q. Wu. Learning to rank using an ensemble of lambda-gradient models. In Yahoo! Learning to Rank Challenge, pages 25-35, 2011.
15. O. Chapelle and Y. Chang. Yahoo! learning to rank challenge overview. In Yahoo! Learning to Rank Challenge, pages 1-24, 2011.
16. E. Diaz-Aviles, H. T. Lam, F. Pinelli, S. Braghin, Y. Gkoufas, M. Berlingerio, and F. Calabrese. Predicting user engagement in twitter with collaborative ranking. In Proceedings of the 2014 Recommender Systems Challenge, RecSysChallenge'14, 2014.
17. E. Diaz-Aviles, C. Orellana-Rodriguez, and W. Nejdl. Taking the pulse of political emotions in latin america based on social web streams. Web Congress, Latin American, 2012.
18. Z. Gantner, S. Rendle, and L. Schmidt-Thieme. Factorization models for context-/time-aware movie recommendations. In Proceedings of the Workshop on Context-Aware Movie Recommendation, CAMRa'10, 2010.
19. rd International Conference on, 2007.
20. X. He, M. Gao, M.-Y. Kan, Y. Liu, and K. Sugiyama. Predicting the popularity of web 2.0 items based on user comments. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'14, pages 233-242, New York, NY, USA, 2014. ACM.
21. A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. In Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys'10, pages 79-86, New York, NY, USA, 2010. ACM.
22. S. Kiritchenko, X. Zhu, and S. M. Mohammad. Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research (JAIR), 2014.
23. Lemur Project. Ranklib. http://www.lemurproject.org/, 2015. Accessed: 2015-03.
24. rd ACM Conference on Hypertext and Social Media, HT'12, 2012.
25. T.-Y. Liu. Learning to Rank for Information Retrieval. Springer, 2011.
26. C. Mettouris and G. A. Papadopoulos. Ubiquitous recommender systems. Computing, 96(3):223-257, 2014.
27. S. M. Mohammad and S. Kiritchenko. Using hashtags to capture fine emotion categories from tweets. Computational Intelligence, 2014.
28. S. M. Mohammad and P. D. Turney. Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 2011.
29. A. Odić, M. Tkalčić, A. Košir, and J. F. Tasić. Relevant context in a movie recommender system: Users' opinion vs. statistical detection. In Proceedings of the 4th Workshop on Context-Aware Recommender Systems, 2012.
30. A. Odić, M. Tkalčić, J. F. Tasić, and A. Košir. Predicting and detecting the relevant contextual information in a movie-recommender system. Interacting with Computers, 25(1), 2013.
31. C. Orellana-Rodriguez, E. Diaz-Aviles, and W. Nejdl. Mining emotions in short films: User comments or crowdsourcing? In Proceedings of the 22nd International Conference on World Wide Web Companion (Posters), WWW'13, 2013.
32. R. Plutchik. A General Psychoevolutionary Theory of Emotion. Academic press, New York, 1980.
33. S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Fast context-aware recommendations with factorization machines. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11. ACM, 2011.
34. Y. Shi, M. Larson, and A. Hanjalic. Mining mood-specific movie similarity with matrix factorization for context-aware recommendation. In Proceedings of the Workshop on Context-Aware Movie Recommendation, CAMRa'10, 2010.
35. J.-M. Xu, X. Zhu, and A. Bellmore. Fast learning for sentiment analysis on bullying. In Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining, WISDOM'12, 2012.
36. D. Yang, D. Zhang, Z. Yu, and Z. Wang. A sentiment-enhanced personalized location recommendation system. In Proceedings of the 24th ACM Conference on Hypertext and Social Media, HT'13, 2013.
37. K. Zhang, S. Bhattacharyya, and S. Ram. Empirical analysis of implicit brand networks on social media. In Proceedings of the 25th ACM Conference on Hypertext and Social Media, HT'14, 2014.
38. rd International Workshop on Human Decision Making in Recommender Systems, 2013.