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Mind reading, A proof of concept


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

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Mind reading, A proof of concept

  2. 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. 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. 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. 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
  7. 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
  9. 9. SENTIMENT ANALYSIS - DRAWBACKS • Language complexities • Cultural dependencies • Thus can not expect high accuracy • Identifying sarcasm and irony
  10. 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. 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. 12. TRENDING TECHNIQUES - A CASE STUDY Mappiness mobile app
  13. 13. TRENDING TECHNIQUES - A CASE STUDY Dialsmith Perception Analyzer
  14. 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. 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. 16. OUR SOLUTION SaaS application
  18. 18. REFERENCES 1. “Perception and the Perceptual Process,” Psychology. [Online]. Available: 2. Saul McLeod, “Likert Scale,”, 2008. [Online]. Available: 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. 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: [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: [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. 20. Thank you!
  21. 21. Q&A