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

Mulholland, E, McKevitt, P, Lunney, TF, Farren, J and Wilson, J (2015) 360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet. In: Proc. of the 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN-2015), Politecnico di Torino, Turin (Torino), Italy. IEEE. 5 pp. [Conference contribution]

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URL: http://dx.doi.org/10.4108/icst.intetain.2015.259631

DOI: 10.4108/icst.intetain.2015.259631


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.

Item Type:Conference contribution (Paper)
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
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Faculty of Arts
Faculty of Arts > School of Creative Arts and Technologies
Research Institutes and Groups:Computer Science Research Institute
ID Code:32365
Deposited By: Professor Paul McKevitt
Deposited On:30 Sep 2015 09:43
Last Modified:30 Sep 2015 09:43

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