학술논문
360-MAM-Affect: Sentiment analysis with the Google prediction API and EmoSenticNet
Document Type
Conference
Source
2015 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN) Intelligent Technologies for Interactive Entertainment (INTETAIN), 2015 7th International Conference on. :217-221 Jun, 2015
Subject
Language
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, SentimentClassifer (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.