A presentation on perception analysis published in Information technology research unit, University of Moratuwa symposium 2013.

1. MIND READING: A SURVEY
AND A PROOF OF
CONCEPT PLATFORM FOR
PERCEPTION ANALYSIS
ANDUN S. L. GUNAWARDANA, PRABHATH S. PATHIRANA,
THILINI S.T. GAMAGE, SACHINTHA R. PONNAMPERUMA,
DR. SHAHANI M. WEERAWARANA

2. WHAT IS PERCEPTION ANALYSIS?
• Perceptions are experience people gain from external
stimuli through their sensory system.
• Capture human perceptions for analytical purposes is the
main challenging, but highly demanded

3. IMPORTANCE OF PERCEPTION ANALYSIS
• Enhancing the performance of Sales and Marketing Sector
• Simplify Decision Making
• Manage Reputation
• Provides a platform for Usability and Acceptance Checking
• Identifying the temporal aspect of perceptions via realtime analysis
• Examining and Simulating the Human Mind

4. EVOLUTION OF PERCEPTION ANALYSIS
• Surveys
• Written or oral
• Efficient tactics like “Likert items” were introduced
• Lot of effort
• Limited participation
• Manual analysis
• Major milestone was introducing WWW in 1989

5. EVOLUTION OF PERCEPTION ANALYSIS CT.
• In Web 2.0 end users has become an active writer as well
• Online voting and rating systems
• Surveys moved to web
• Online shopping sites/blogs
• With the growth of web content, the manual processing
became cumbersome task

6. WEB BASED PERCEPTION CAPTURING MECHANISMS

7. SENTIMENT ANALYSIS
• Most of the web content are textual
• Manual analysis is labor and time consuming
• Thus automate text analysis using NLP techniques
• Sentiment measured and mapped to a numerical
value.
• Desired since text can contain lots of information for
different features

8. SENTIMENT ANALYSIS CT.

9. SENTIMENT ANALYSIS - DRAWBACKS
• Language complexities
• Cultural dependencies
• Thus can not expect high accuracy
• Identifying sarcasm and irony

10. BIOMETRICS BASED APPROACHES
• Electromagnetics sensors attached to the body
• XPOD
• Analyzing user experience of video games.
• Facial emotion recognition
• Voice based emotion recognition
• Facial and voice based hybrid approaches
• Video-imaging-based heart rate measurement
• Capturing audience experience

11. TRENDING TECHNIQUES
Trending techniques focus on new angles of
perception capturing & analysis
• Explicit perception sharing
• Real time perception capturing and analysis
• Perception analysis based social networks

12. TRENDING TECHNIQUES - A CASE STUDY
Mappiness mobile app

13. TRENDING TECHNIQUES - A CASE STUDY
Dialsmith Perception Analyzer

14. CROWDSOURCING – IMPACT FOR PERCEPTION
CAPTURING & ANALYSIS
• Provides access to a huge population of people who are
interested in participating in web-based or mobile based
tasks at their own convenience1
• Rapid development of internet and other communication
technologies has made crowdsourcing very effective
• Crowdsourcing can be used for not only collecting data but
also to do analytical tasks
• Mobile crowdsourcing mechanisms can be used for
situations where real-time participation is important2,3

15. CROWDSOURCING - CHALLENGES
General challenges,
•
Drawing the users
•
Privacy and ethical issues
•
Maintaining the quality of data while tracking bad behaviors (e.g. spamming, false inputs)
•
Understanding the knowledge and skills of the target user
For mobile crowdsourcing,
•
Limited battery power and high network cost
•
Assumptions like users has access to their phones all the time are not valid all the time1.
•
people are not equally capable of participating in all situations1.

16. OUR SOLUTION
SaaS
application

17. PROPOSED ARCHITECTURE

18. REFERENCES
1.
“Perception and the Perceptual Process,” About.com Psychology. [Online]. Available:
http://psychology.about.com/od/sensationandperception/ss/perceptproc_2.htm.
2.
Saul McLeod, “Likert Scale,” http://www.simplypsychology.org, 2008. [Online]. Available:
http://www.simplypsychology.org/likert-scale.html.
3.
M. Cooke and N. Buckley, “Web 2.0, social networks and the future of market research,” International Journal of Market
Research, vol. 50, no. 2, p. 267, 2008.
4.
“The Truth About Sentiment & Natural Language Processing « Synthesio,” Synthesio, Mar. 2011
5.
R. Feldman, “Techniques and applications for sentiment analysis,” Communications of the ACM, vol. 56, no. 4, p. 82, Apr. 2013.
6.
Kumar and T. M. Sebastian, “Sentiment Analysis: A Perspective on its Past, Present and Future,” International Journal of
Intelligent Systems and Applications (IJISA), vol. 4, no. 10, p. 1, 2012.
7.
S. Dornbush, K. Fisher, K. McKay, A. Prikhodko, and Z. Segall, “XPOD-A human activity and emotion aware mobile music player,”
2005.
8.
P. Mirza-Babaei, S. Long, E. Foley, and G. McAllister, “Understanding the Contribution of Biometrics to Games User Research,” in
Proc. DIGRA, 2011.
9.
M. Wöllmer, A. Metallinou, F. Eyben, B. Schuller, and S. Narayanan, “Context-sensitive multimodal emotion recognition from
speech and facial expression using bidirectional lstm modeling,” in Proceedings of the Annual Conference of the International
Speech Communication Association (ISCA), Interspeech, 2010, pp. 2362–2365.
10. Y.-Y. Fan and R. Weber, “Capturing audience experience via mobile biometrics,” 2012.
[Accessed: 05-Apr-2013].

19. REFERENCES
11.
F. Ortag and H. Huang, “Location-based emotions relevant for pedestrian navigation,” in Proceedings of the 25th
international cartographic conference, Paris, 2011.
12.
K. Church, E. Hoggan, and N. Oliver, “A study of mobile mood awareness and communication through MobiMood,”
in Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries, 2010, pp.
128–137
13.
J. Oh and G. Wang, “Audience-participation techniques based on social mobile computing,” in International
Computer Music Conference, ICMC, 2011.
14.
Dialsmith and the Perception Analyzer - Advanced Research Solutions.‖ [Online]. Available:
http://www.perceptionanalyzer.com/products/perception-analyzer.html. [Accessed: 03-Apr-2013]
15.
Dialsmith‘s Perception Analyzer tool used by CNN researchers for 2012 Presidential Debates, Primaries, and
Conventions - DIAL . LOG.‖ [Online]. Available:
http://perceptionanalyzer.typepad.com/perception_analyzer/2012/10/perception-analyzer-by-dialsmithfeatured-on-cnn-for-2012-presidential-debates.html. [Accessed: 03-Apr-2013].
16.
L. Schmidt, “Crowdsourcing for human subjects research,” in CrowdConf’10 Proceedings of the 1st International
Conference on Crowdsourcing, 2010.
17.
A. Brew, D. Greene, and P. Cunningham, “Using crowdsourcing and active learning to track sentiment in online
media,” ECAI 2010, pp. 145–150, 2010.
18.
M. Millar and D. A. Dillman, “Encouraging Survey Response via Smartphones,” Survey Practice, vol. 5, no. 3, 2012.
19.
T. D. Buskirk and C. Andres, “Smart Surveys for Smart Phones: Exploring Various Approaches for Conducting Online
Mobile Surveys via Smartphones*,” Survey Practice, vol. 5, no. 1, 2013.
20.
1. B. A. Campbell, C. C. Tossell, M. D. Byrne, and P. Kortum, “Voting on a Smartphone Evaluating the Usability of
an Optimized Voting System for Handheld Mobile Devices,” Proceedings of the Human Factors and Ergonomics
Society Annual Meeting, vol. 55, no. 1, pp. 1100–1104, Sep. 2011..

20. Thank you!

21. Q&A