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

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

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 t...

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Bibliographic Details
Main Authors: Eleanor Mulholland, Paul Mc Kevitt, Tom Lunney, John Farren, Judy Wilson
Format: Article
Language:English
Published: European Alliance for Innovation (EAI) 2015-08-01
Series:EAI Endorsed Transactions on Scalable Information Systems
Subjects:
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
Online Access:http://eudl.eu/doi/10.4108/icst.intetain.2015.259631

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