sis 15(6): e5

Research Article

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

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  • @ARTICLE{10.4108/icst.intetain.2015.259631,
    author={Eleanor Mulholland and Paul Mc Kevitt and Tom Lunney and John Farren and Judy Wilson},
    title={360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet},
    journal={EAI Endorsed Transactions on Scalable Information Systems},
    volume={2},
    number={6},
    publisher={EAI},
    journal_a={SIS},
    year={2015},
    month={8},
    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},
    doi={10.4108/icst.intetain.2015.259631}
}
  • Eleanor Mulholland
    Paul Mc Kevitt
    Tom Lunney
    John Farren
    Judy Wilson
    Year: 2015
    360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet
    SIS
    EAI
    DOI: 10.4108/icst.intetain.2015.259631
Eleanor Mulholland1,*, Paul Mc Kevitt1, Tom Lunney1, John Farren2, Judy Wilson2
  • 1: Ulster University, School of Creative Arts & Technologies
  • 2: 360 Production Ltd.
*Contact email: mulholland-e9@email.ulster.ac.uk

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.

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
Published
2015-08-03
Publisher
EAI
http://dx.doi.org/10.4108/icst.intetain.2015.259631

Copyright © 2015 E. Mulholland et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.