1. 360-MAM-Affect: Sentiment Analysis with the Google Prediction API and EmoSenticNet
Eleanor Mulholland, Paul Mc Kevitt & Tom Lunney
School of Creative Arts & Technologies 
Ulster University
Derry/Londonderry, Northern Ireland
John Farren & Judy Wilson
360 Production Ltd.
Suite 2.2, The Innovation Centre

2. Introduction Aims & Objectives Background & Literature Review
Design of 360-MAM-Select
Software Analysis for 360-MAM-Select
SentimentClassifer
EmoSenticNetClassifer
Relation to Other Work
Conclusion & Future Work
Acknowledgements

3. Aims & Objectives Implement online recommender system (360-MAM-Select)
Employ sentiment analysis & gamification to achieve
higher quality video recommendations
Sentiment Analysis facilitates video recommendations
Gamification motivates engagement with video content

4. Recommender Systems Rank products against others for comparison
Faridani (2011) trained a recommender model with
ratings from OpinionSpace dataset
Hanser et al. (2010) implemented NewsViz
giving numerical emotion ratings to words
Tkalčič et al. (2011) proposed a unifying framework for
emotion detection

5. Unifiying Framework (Tkalčič et al. 2011)

6. Sentiment Analysis
Recognising negative, positive & neutral opinions (Wilson et al. 2005)
Methods of opinion collecting
Variety of data on the Internet in many different forms
Online information is not static (Khan et al. 2009)

7. Gamification Game mechanics & game design techniques used to enhance
non-game scenarios (Deterding et al. 2011)
Common gamification methods (Lee et al. 2013)
Gamification is popular for monitoring & analysing
online communities (Bista et al. 2012)
Gamification has improved learning & information retention
in education & staff training (Landers & Callan, 2011)

8. Design of 360-MAM-Select
Modules for sentiment analysis & emotion modelling (360-MAM-Affect) & gamification (360-MAM-Gamify)
How user responds emotionally to media content
360-MAM-Affect harvests YouTube & Head Squeeze (Head Squeeze, 2014) comments on video content
Identify the overall reception

9. Architecture of 360-MAM-Select

10. Software Analysis for 360-MAM-Select
Google Prediction API (Google, 2015) used to create 2 sentiment analysis prediction models:
(1) SentimentClassifer & (2) EmoSenticNetClassifer
SentimentClassifer model trained with Google Prediction API with classification accuracy of 61%
EmoSenticNetClassifer model trained with Google Prediction API, using EmoSenticNet (Gelbukh, 2015) with classification accuracy of 59%

11. Testing SentimentClassifier

12. EmoSenticNet Example Values

13. EmoSenticNet Concepts Associated With One Emotion

14. Frequencies (%) of 2 Emotions Per Concept

15. Combination of 3 Emotions Per Concept

16. EmoSenticNet Model Test
EmoSenticNetClassifer model returned 7 number labels
(Anger, Disgust, Joy, Sad, Surprise, Fear & Neutral) with a classification accuracy of 59%
7 concepts failed to return highest rating in expected emotion label
Joy had highest frequency of entries in training data

17. EmoSenticNetClassifer Prediction Results

18. Relation to Other Work
Recommender systems (Ricci et al. 2011) ability to personalise experiences (Linden et al. 2003) to provide high quality recommendations (Bartłomiej 2005)
Emotion has been identified as important factor in improving recommender systems (Tkalčič et al. 2011)
360-MAM-Select will advance recommender systems
by providing improved user experience

19. Conclusion & Future Work
Architecture of 360-MAM-Select
Testing SentimentClassifer (Google Prediction API) & EmoSenticNetClassifer (Google Prediction API + EmoSenticNet)
Investigate how sentiment analysis & gamification can be utilised
Improve user participation & video retrieval
Implementation & testing of 360-MAM-Select

20. Acknowledgements Dr. Brian Bridges Dr. Kevin Curran
Dr. Lisa Fitzpatrick
360 Production Ltd. & Alleycats TV