360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet - Mulholland, Eleanor; Kevitt, Paul Mc; Lunney, Tom; Farren, John; Wilson, Judy (EAI Endorsed Transactions on Scalable Information Systems, 2015)
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