Analysing emotional sentiment in people's YouTube channel comments

E Mulholland, P McKevitt, TF Lunney, K-M Schneider

Research output: Chapter in Book/Report/Conference proceedingChapter

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].
LanguageEnglish
Title of host publicationInteractivity, 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)
EditorsA.L. Brooks, E. Brooks
Place of PublicationBerlin
Pages181-188
Volume196
DOIs
Publication statusPublished - 18 Mar 2017

Fingerprint

Recommender systems
Asset management
Application programming interfaces (API)
Feedback
Testing
Processing

Keywords

  • 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.

Cite this

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. https://doi.org/10.1007/978-3-319-55834-9_21
Mulholland, E ; McKevitt, P ; Lunney, TF ; Schneider, K-M. / Analysing emotional sentiment in people's YouTube channel comments. 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). editor / A.L. Brooks ; E. Brooks. Vol. 196 Berlin, 2017. pp. 181-188
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Mulholland, E, McKevitt, P, Lunney, TF & Schneider, K-M 2017, Analysing emotional sentiment in people's YouTube channel comments. in AL 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, Berlin, pp. 181-188. https://doi.org/10.1007/978-3-319-55834-9_21

Analysing emotional sentiment in people's YouTube channel comments. / Mulholland, E; McKevitt, P; Lunney, TF; Schneider, K-M.

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). ed. / A.L. Brooks; E. Brooks. Vol. 196 Berlin, 2017. p. 181-188.

Research output: Chapter in Book/Report/Conference proceedingChapter

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T1 - Analysing emotional sentiment in people's YouTube channel comments

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AU - Schneider, K-M

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N2 - 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].

AB - 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].

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Mulholland E, McKevitt P, Lunney TF, Schneider K-M. Analysing emotional sentiment in people's YouTube channel comments. In Brooks AL, Brooks E, editors, 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. Berlin. 2017. p. 181-188 https://doi.org/10.1007/978-3-319-55834-9_21