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Analysing Emotional Sentiment in People’s YouTube Channel Comments

Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 196)


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].


  • 360-MAM-Affect
  • 360-MAM-Select
  • Affective computing
  • Brit Lab
  • EmoSenticNet
  • Gamification
  • Google YouTube API
  • Head Squeeze
  • Machine learning
  • Natural language processing
  • Recommender system
  • Sentiment analysis
  • YouTube
  • YouTube EDU

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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.

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Correspondence to Eleanor Mulholland .

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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.

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