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

E Mulholland; P McKevitt; TF Lunney; J Farren; J Wilson

doi:10.4108/icst.intetain.2015.259631

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

E Mulholland, P McKevitt, TF Lunney, J Farren, J Wilson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

75 Downloads (Pure)

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.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE
Pages	1-5
Number of pages	5
DOIs	https://doi.org/10.4108/icst.intetain.2015.259631
Publication status	Published (in print/issue) - 3 Aug 2015
Event	Proc. of the 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN-2015) - Politecnico di Torino, Turin (Torino), Italy Duration: 3 Aug 2015 → …

Conference

Conference	Proc. of the 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN-2015)
Period	3/08/15 → …

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

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@inproceedings{6c4b3168d1194121a8309cb3ce94b2a4,

title = "360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet",

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.",

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",

author = "E Mulholland and P McKevitt and TF Lunney and J Farren and J Wilson",

year = "2015",

month = aug,

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doi = "10.4108/icst.intetain.2015.259631",

language = "English",

pages = "1--5",

booktitle = "Unknown Host Publication",

publisher = "IEEE",

address = "United States",

note = "Proc. of the 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN-2015) ; Conference date: 03-08-2015",

}

Mulholland, E, McKevitt, P, Lunney, TF, Farren, J & Wilson, J 2015, 360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet. in Unknown Host Publication. IEEE, pp. 1-5, Proc. of the 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN-2015), 3/08/15. https://doi.org/10.4108/icst.intetain.2015.259631

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AU - Mulholland, E

AU - McKevitt, P

AU - Lunney, TF

AU - Farren, J

AU - Wilson, J

PY - 2015/8/3

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N2 - 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.

AB - 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.

KW - affective computing

KW - emosenticnet

KW - gamification

KW - google prediction api

KW - head squeeze

KW - machine learning

KW - natural language processing

KW - recommender system

KW - sentiment analysis

KW - youtube

KW - 360-mam-affect

KW - 360-mam-select

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DO - 10.4108/icst.intetain.2015.259631

M3 - Conference contribution

SP - 1

EP - 5

BT - Unknown Host Publication

PB - IEEE

T2 - Proc. of the 7th International Conference on Intelligent Technologies for Interactive Entertainment (INTETAIN-2015)

Y2 - 3 August 2015

ER -

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

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