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

2 Citations (Scopus)
8 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages1-5
Number of pages5
DOIs
Publication statusPublished - 3 Aug 2015
EventProc. of the 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN-2015) - Politecnico di Torino, Turin (Torino), Italy
Duration: 3 Aug 2015 → …

Conference

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

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

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  • 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). IEEE. https://doi.org/10.4108/icst.intetain.2015.259631