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

EAI Endorsed Transactions on Scalable Information Systems

Paul Mc Kevitt¹  Judy Wilson²  Eleanor Mulholland³  John Farren³  Tom Lunney³ 

[1] [e-mail];360 Production Ltd.;Ulster University, School of Creative Arts & Technologies;

关键词: 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

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown