360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet

E Mulholland, P McKevitt, TF Lunney, J Farren, J Wilson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Online recommender systems are useful for media asset management where they select the best content from a set of media assets. We have developed an architecture for 360-MAM- Select, a recommender system for educational video content. 360-MAM-Select will utilise sentiment analysis and gamification techniques for the recommendation of media assets. 360-MAM-Select will increase user participation with digital content through improved video recommendations. Here, we discuss the architecture of 360-MAM-Select and the use of the Google Prediction API and EmoSenticNet for 360-MAM-Affect, 360-MAM-Select's sentiment analysis module. Results from testing two models for sentiment analysis, Sentiment Classifier (Google Prediction API) and EmoSenticNetClassifer (Google Prediction API + EmoSenticNet) are promising. Future work includes the implementation and testing of 360-MAM-Select on video data from YouTube EDU and Head Squeeze.

Conference

ConferenceProc. of the 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN-2015)
Period3/08/15 → …

Fingerprint

Application programming interfaces (API)
Recommender systems
Asset management
Testing
Classifiers

Keywords

  • affective computing
  • emosenticnet
  • gamification
  • google prediction api
  • head squeeze
  • machine learning
  • natural language processing
  • recommender system
  • sentiment analysis
  • youtube
  • 360-mam-affect
  • 360-mam-select

Cite this

Mulholland, E., McKevitt, P., Lunney, TF., Farren, J., & Wilson, J. (2015). 360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet. In Unknown Host Publication (pp. 1-5) https://doi.org/10.4108/icst.intetain.2015.259631
Mulholland, E ; McKevitt, P ; Lunney, TF ; Farren, J ; Wilson, J. / 360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet. Unknown Host Publication. 2015. pp. 1-5
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Mulholland, E, McKevitt, P, Lunney, TF, Farren, J & Wilson, J 2015, 360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet. in Unknown Host Publication. pp. 1-5, Proc. of the 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN-2015), 3/08/15. https://doi.org/10.4108/icst.intetain.2015.259631

360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet. / Mulholland, E; McKevitt, P; Lunney, TF; Farren, J; Wilson, J.

Unknown Host Publication. 2015. p. 1-5.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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