Research Article
360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet
@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
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.
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.