Skip to main content
SpringerLink
Log in

Analysing Emotional Sentiment in People’s YouTube Channel Comments

  • Conference paper
  • First Online:
Interactivity, Game Creation, Design, Learning, and Innovation (ArtsIT 2016, DLI 2016)

Abstract

Online recommender systems are useful for media asset management where they select the best content from a set of media assets. We are developing a recommender system called 360-MAM-Select for educational video content. 360-MAM-Select utilises sentiment analysis, emotion modeling and gamification techniques applied to people’s comments on videos, for the recommendation of media assets. Here, we discuss the architecture of 360-MAM-Select, including its sentiment analysis module, 360-MAM-Affect and gamification module, 360-Gamify. 360-MAM-Affect is implemented with the YouTube API [9], GATE [5] for natural language processing, EmoSenticNet [8] for identifying emotion words and RapidMiner [20] to count the average frequency of emotion words identified. 360-MAM-Affect is tested by tagging comments on the YouTube channels, Brit Lab/Head Squeeze [3], YouTube EDU [28], Sam Pepper [22] and MyTop100Videos [18] with EmoSenticNet [8] in order to identify emotional sentiment. Our results show that Sad, Surprise and Joy are the most frequent emotions across all the YouTube channel comments. Future work includes further implementation and testing of 360-MAM-Select deploying the Unifying Framework [25] and Emotion-Imbued Choice (EIC) model [13] within 360-MAM-Affect for emotion modelling, by collecting emotion feedback and sentiment from users when they interact with media content. Future work also includes implementation of the gamification module, 360-Gamify, in order to check its suitability for improving user participation with the Octalysis gamification framework [4].

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

Access this chapter

Log in via an institution
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 EPUB and 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

References

  1. Bing, L.: AI and opinion mining. IEEE Intell. Syst. 25(3), 76–80 (2010)

    Google Scholar 

  2. Blair, O.: Sam pepper heavily criticised for vile fake murder prank video. http://www.independent.co.uk/news/people/sam-pepper-criticised-over-vile-prank-fake-murder-video-a6754861.html. Accessed 15 Dec 2015

  3. Brit Lab/Head Squeeze.: 360 Production. https://www.youtube.com/user/HeadsqueezeTV. Accessed 15 Dec 2015

  4. Chou, Y.K.: Actionable Gamification: Beyond Points, Badges, and Leaderboards. Leanpub, Fremont (2015)

    Google Scholar 

  5. Cunningham, H.: General Architecture for Text Engineering (GATE). https://gate.ac.uk/. Accessed 10 Dec 2015

  6. Downes, G., McKevitt, P., Lunney, T., Farren, J., Ross, C.: 360-PlayLearn: gamification and game-based learning for virtual learning environments on interactive television. In: Walshe, R., Perrin, D., Cunningham, P. (eds.) Proceedings of the 23rd Irish Conference on Artificial Intelligence and Cognitive Science (AICS-2012), Carlton Hotel, Dublin Airport, Dublin, Ireland, 17–19 September 2012, pp. 116–121. Logos Verlag, Berlin (2012)

    Google Scholar 

  7. Faridani, S.: Using canonical correlation analysis for generalized sentiment analysis, product recommendation and search. In: Proceedings of the Fifth ACM Conference on Recommender Systems, Chicago, Illinois, USA, 23–27 October, pp. 355–358 (2011)

    Google Scholar 

  8. Gelbukh, A.: EmoSenticNet. http://www.gelbukh.com/emosenticnet/. Accessed 20 Feb 2014

  9. Google: YouTube API: Google Developer’s Guide. https://developers.google.com/youtube/. Accessed 17 Nov 2015

  10. Hanser, E., McKevitt, P., Lunney, T., Condell, J.: NewsViz: emotional visualization of news stories. In: Inkpen, D., Strapparava, C. (eds.) Proceedings of the NAACL-HLT Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, Millennium Biltmore Hotel, Los Angeles, CA, USA, 5 June, pp. 125–130 (2010)

    Google Scholar 

  11. Kant, V., Bharadwaj, K.K.: Integrating collaborative and reclusive methods for effective recommendations: a fuzzy Bayesian approach. Int. J. Intell. Syst. 28(11), 1099–1123 (2013)

    Article  Google Scholar 

  12. Khan, K., Baharudin, B.B., Khan, A., e-Malik, F.: Mining opinion from text documents: a survey. In: Proceedings of the 3rd IEEE International Conference on Digital Ecosystems and Technologies, Istanbul, Turkey, 1–3 June, pp. 217–222 (2009)

    Google Scholar 

  13. Lerner, J.S., Li, Y., Valdesolo, P., Kassam, K.: Emotion and decision making. Annu. Rev. Psychol. 66, 799–823 (2015). (Supplemental Materials)

    Article  Google Scholar 

  14. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  15. Martin, B.: 2015 UK Digital Future in Focus (whitepaper). 2015 Digital Future in Focus. https://www.comscore.com/Insights/Blog/2015-Europe-Digital-Future-in-Focus. Accessed 17 Dec 2015

  16. Mayer, J.D., Gaschke, Y.N.: The experience and meta-experience of mood. J. Pers. Soc. Psychol. 55(1), 102–111 (1988)

    Article  Google Scholar 

  17. Mulholland, E., McKevitt, P., Lunney, T., Farren, J., Wilson, J.: 360-MAM-Affect: sentiment analysis with the Google prediction API and EmoSenticNet. In: Proceedings of the 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN-2015), Politecnico di Torino, Turin (Torino), Italy, 10–12 June, pp. 1–5 (2015)

    Google Scholar 

  18. MyTop10Videos: MyTop100Videos. https://www.youtube.com/user/MyTop10Videos. Accessed 15 Dec 2015

  19. Nasukawa, T., Yi, J.: Sentiment analysis: capturing favourability using natural language processing. In: Proceedings of the 2nd International Conference on Knowledge Capture, Sanibel Island, FL, USA, 23–25 October, pp. 70–77 (2003)

    Google Scholar 

  20. RapidMiner: RapidMiner. https://rapidminer.com/. Accessed 4 May 2015

  21. Ricci, F., Rokach, L., Shapira, B.: Recommender Systems Handbook. Springer Press, New York (2011)

    Book  MATH  Google Scholar 

  22. Sam Pepper: Sam. https://www.youtube.com/user/OFFICIALsampepper. Accessed 15 Dec 2015

  23. Śnieżyński, B.: Recommendation system using multistrategy inference and learning. In: Niewiadomski, A., Kacprzyk, J., Szczepaniak, P.S. (eds.) Advances in Web Intelligence, pp. 421–426. Springer, Berlin (2005)

    Google Scholar 

  24. Strapparava, C., Valitutti, A.: WordNet-Affect: an affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004), Lisbon, Portugal, 26–28 May, pp. 1083–1086 (2004)

    Google Scholar 

  25. Tkalčič, M., Košir, A., Tasič, J.: Affective recommender systems: the role of emotions in recommender systems. In: Proceedings of the RecSys 2011 Workshop Human Decision Making in Recommender Systems (Decisions@RecSys 2011), Chicago, Illinois, 23–27 October, pp. 9–13 (2011)

    Google Scholar 

  26. Tzanis, G., Katakis, I., Partalas, I., Vlahavas, I.: Modern applications of machine learning. In: Proceedings of the 1st Annual SEERC Doctoral Student Conference – DSC 2006, 1(1), Thessaloniki, Greece, 10 July, pp. 1–10 (2006)

    Google Scholar 

  27. Wilson, T., Wiebe, J., Hoffmann, P.: Recognising contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT 2005), Vancouver, British Columbia, Canada, 6–8 October, pp. 347–354 (2005)

    Google Scholar 

  28. YouTube EDU: YouTube EDU. https://www.youtube.com/channel/UC3yA8nDwraeOfnYfBWun83g. Accessed 15 Dec 2015

  29. YouTube: YouTube Statistics. http://www.youtube.com/yt/press/statistics.html. Accessed 17 Mar 2015

  30. Zheng, Y., Burke, R., Mobasher, B.: The role of emotions in context-aware recommendation. In: Proceedings of the 3rd International Workshop on Human Decision Making in Recommender Systems, ACM, Hong Kong, China, 12 October, pp. 21–28 (2013)

    Google Scholar 

Download references

Acknowledgments

We wish to thank Dr. Brian Bridges, Dr. Kevin Curran and Dr. Lisa Fitzpatrick at Ulster University, John Farren and Judy Wilson at 360 Production Ltd. and Alleycats TV for their useful suggestions on this work. This research is funded by a Northern Ireland Department of Employment & Learning (DEL) Co-operative Awards in Science & Technology (CAST) Ph.D. Studentship Awardat Ulster University.

Author information

Authors and Affiliations

  1. School of Creative Arts and Technologies, Ulster University, Derry/Londonderry, Northern Ireland, UK

    Eleanor Mulholland, Paul Mc Kevitt & Tom Lunney

  2. Google Ireland Ltd., Barrow Street, Dublin, Ireland

    Karl-Michael Schneider

Authors
  1. Eleanor Mulholland
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul Mc Kevitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tom Lunney
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Karl-Michael Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Eleanor Mulholland .

Editor information

Editors and Affiliations

  1. Department of Architecture, Design and Media Technology (CREATE), Aalborg University, Aalborg/Esbjerg, Denmark

    Anthony L. Brooks

  2. Department of Learning and Philosophy, The Faculty of Humanities, Aalborg University, Aalborg, Denmark

    Eva Brooks

Rights and permissions

Reprints and permissions

Copyright information

© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Cite this paper

Mulholland, E., Mc Kevitt, P., Lunney, T., Schneider, KM. (2017). Analysing Emotional Sentiment in People’s YouTube Channel Comments. In: Brooks, A., Brooks, E. (eds) Interactivity, Game Creation, Design, Learning, and Innovation. ArtsIT DLI 2016 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 196. Springer, Cham. https://doi.org/10.1007/978-3-319-55834-9_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-55834-9_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55833-2

  • Online ISBN: 978-3-319-55834-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation