Research Article
360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet
@INPROCEEDINGS{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}, proceedings={7th International Conference on Intelligent Technologies for Interactive Entertainment}, publisher={EAI}, proceedings_a={INTETAIN}, 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
INTETAIN
ICST
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.
