Skip to main content

Stylistic Features for Affect-Based Movie Recommendations

  • Conference paper
Human Behavior Understanding (HBU 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8212))

Included in the following conference series:

Abstract

In recent years, studies have estimated affective movie content computationally with stylistic features, yet knowledge of the perceptual relation between style and affect remains scarce. Such knowledge would be useful in affect-based movie recommendation systems. To this end, a user study was conducted in which seventy-three participants with varying levels of film expertise rated movie clips according to 13 stylistic features in three modalities (visual, aural and temporal) as well as perceived and felt affect in three dimensions (hedonic tone, energetic arousal and tense arousal). Style-based linear regression models were then constructed for each affect dimension. Visual features contributed the most to hedonic tone and tense arousal, and temporal features to energetic arousal. Also, perceived affect showed greater inter-rater agreement and better modeling performance than felt affect. The results indicate that the influence of specific stylistic features on affect varies by dimension and by whether the affect is perceived or felt.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Benini, S., Canini, L., Leonardi, R.: A Connotative Space for Supporting Movie Affective Recommendation. IEEE Transactions on Multimedia 13(6), 1356–1370 (2011)

    Article  Google Scholar 

  2. Bordwell, D.: The Way Hollywood Tells It. University of California Press (2006)

    Google Scholar 

  3. Bordwell, D., Thompson, K.: Film Art: An Introduction. McGraw-Hill, Inc. (1990)

    Google Scholar 

  4. Carroll, N.: Film, Emotion, and Genre. In: Plantinga, C., Smith, G.M. (eds.) Passionate Views: Film, Cognition, and Emotion, pp. 21–47. The John Hopkins University Press (1999)

    Google Scholar 

  5. Cohen, J.: Statistical Power Analysis for the Behavioral Sciences, 2nd edn. Routledge Academic (1988)

    Google Scholar 

  6. Gabrielsson, A., Juslin, P.: Emotional Expression in Music. In: Davidson, R.J., Scherer, K.R., Goldsmith, H.H. (eds.) Handbook of Affective Sciences, pp. 503–534. Oxford University Press (2003)

    Google Scholar 

  7. Grahn, J.A., Brett, M.: Rhythm and Beat Perception in Motor Areas of the Brain. Journal of Cognitive Neuroscience 19(5), 893–906 (2007)

    Article  Google Scholar 

  8. Ilie, G., Thompson, W.F.: A Comparison of Acoustic Cues in Music and Speech for Three Dimensions of Affect. Music Perception 23(4), 319–330 (2006)

    Article  MathSciNet  Google Scholar 

  9. IMDb.com, Inc.: IMDb, http://www.imdb.com

  10. John, O.P., Naumann, L.P., Soto, C.J.: Paradigm Shift to the Integrative Big Five Trait Taxonomy. In: John, O.P., Robbins, R.W., Pervin, L.A. (eds.) Handbook of Personality, pp. 114–156. Guilford, New York (2008)

    Google Scholar 

  11. Kallinen, K., Rajava, N.: Emotion Perceived and Emotion Felt: Same and Different. Musicae Scientiae 10(2), 191–213 (2006)

    Article  Google Scholar 

  12. Knautz, K., Stock, W.G.: Collective Indexing of Emotions in Videos. Journal of Documentation 67(6), 975–994 (2011)

    Article  Google Scholar 

  13. Lewis, M., Haviland-Jones, J.: Handbook of Emotions, 2nd edn. Guilford (2000)

    Google Scholar 

  14. Mackendrick, A.: On Film-Making. Faber & Faber, Ltd. (2006)

    Google Scholar 

  15. Matthews, G., Jones, D.M., Chamberlain, A.G.: Refining the Measurement of Mood: The UWIST Mood Adjective Checklist. British Journal of Psychology 81(1), 17–42 (1990)

    Article  Google Scholar 

  16. Murray, I.R., Arnott, J.L.: Toward the Simulation of Emotion in Synthetic Speech. Journal of the Acoustical Society of America 93(2), 1097–1108 (1993)

    Article  Google Scholar 

  17. Posner, J., Russell, J.A., Peterson, B.S.: The Circumplex Model of Affect: An Integrative Approach to Affective Neuroscience, Cognitive Development, and Psychopathology. Development and Psychopathology 17(3), 715–734 (2005)

    Article  Google Scholar 

  18. Russell, J.A.: A Circumplex Model of Affect. Journal of Personality and Social Psychology 39(6), 1161–1178 (1980)

    Article  Google Scholar 

  19. Russell, J.A., Weiss, A., Mendelsohn, G.A.: Affect Grid: A Single-item Scale of Pleasure and Arousal. Journal of Personality and Social Psychology 57(3), 493–502 (1989)

    Article  Google Scholar 

  20. Schimmack, U., Rainer, R.: Experiencing Activation: Energetic Arousal and Tense Arousal Are Not Mixtures of Valence and Activation. Emotion 2(4), 412–417 (2002)

    Article  Google Scholar 

  21. Simons, R.F., Detenber, B.H., Roedema, T.M., Reiss, J.E.: Emotion Processing in Three Systems: The Medium and the Message. Psychophysiology 36(5), 619–627 (1999)

    Article  Google Scholar 

  22. Soleymani, M., Kierkels, J.J., Chanel, G., Pun, T.: A Bayesian Framework for Video Affective Representation. In: 3rd IEEE International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–7. IEEE (2009)

    Google Scholar 

  23. Spottiswoode, R.: A Grammar of the Film: An Analysis of Film Technique. University of California Press (1950)

    Google Scholar 

  24. Tan, E.S.: Emotion and the Structure of Narrative Film. Routledge (1996)

    Google Scholar 

  25. Teixeira, R.M., Yamasaki, T., Aizawa, K.: Comparative Analysis of Low-Level Visual Features for Affective Determination of Video Clips. In: 5th IEEE International Conference on Future Information Technology, pp. 1–6. IEEE (2010)

    Google Scholar 

  26. Thayer, R.E.: The Biopsychology of Mood and Arousal. Oxford University Press (1989)

    Google Scholar 

  27. Valdez, P., Mehrabian, A.: Effects of Color on Emotions. Journal of Experimental Psychology 123(4), 394–409 (1994)

    Article  Google Scholar 

  28. Zhang, S., Tian, Q., Huang, Q., Gao, W., Li, S.: Utilizing Affective Analysis for Efficient Movie Browsing. In: 16th IEEE International Conference on Image Processing, pp. 1853–1856. IEEE (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Tarvainen, J., Westman, S., Oittinen, P. (2013). Stylistic Features for Affect-Based Movie Recommendations. In: Salah, A.A., Hung, H., Aran, O., Gunes, H. (eds) Human Behavior Understanding. HBU 2013. Lecture Notes in Computer Science, vol 8212. Springer, Cham. https://doi.org/10.1007/978-3-319-02714-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02714-2_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02713-5

  • Online ISBN: 978-3-319-02714-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics