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 , GATE  for natural language processing, EmoSenticNet  for identifying emotion words and RapidMiner  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 , YouTube EDU , Sam Pepper  and MyTop100Videos  with EmoSenticNet  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  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 .
|Title of host publication||Interactivity, Game Creation, Design, Learning, and Innovation, Proc. of the 5th EAI International Conference on ArtsIT, Interactivity and Game Creation (ArtsIT-2016) and 1st EAI International Conference on Design, Learning and Innovation (DLI-2016)|
|Editors||A.L. Brooks, E. Brooks|
|Place of Publication||Berlin|
|Publication status||Published - 18 Mar 2017|
- affective computing
- Brit Lab
- Google YouTube API
- Head Squeeze
- machine learning
- natural language processing
- recommender system
- sentiment analysis
- YouTube EDU.
Mulholland, E., McKevitt, P., Lunney, TF., & Schneider, K-M. (2017). Analysing emotional sentiment in people's YouTube channel comments. In A. L. Brooks, & E. Brooks (Eds.), Interactivity, Game Creation, Design, Learning, and Innovation, Proc. of the 5th EAI International Conference on ArtsIT, Interactivity and Game Creation (ArtsIT-2016) and 1st EAI International Conference on Design, Learning and Innovation (DLI-2016) (Vol. 196, pp. 181-188). Berlin: Springer. https://doi.org/10.1007/978-3-319-55834-9_21