research-article

Understanding human perceptual experience in unstructured data on the web

Authors:

Kyoung-Sook Kim,

Hirotaka OgawaAuthors Info & Claims

WI '17: Proceedings of the International Conference on Web Intelligence

Pages 491 - 498

https://doi.org/10.1145/3106426.3106430

Published: 23 August 2017 Publication History

Abstract

Computing for human experience has become more important for understanding all of aspects of any interaction of human beings in the cyber, physical, and social environments. In particular, artificial intelligent technologies based on big data enable to understand natural language, enhance day to day human experience, and make a better decision. In this paper, we propose a method to classify unstructured text data on the Web into the five types of sensation features: sight (ophthalmoception), hearing (audioception), touch (tactioception), smell (olfacception), and taste (gustaoception). Even though sensation is the first process of human experience against the environments, the study of sensation information extraction is neglected due to lack of sensory expression and knowledge comparing with the sentimental analysis or opinion mining. We first define the sensation measurement that is assigned to each feature. Then, we identify which sensation feature has a strong influence on human perceptual experience in a specific topic of corpus. Finally, we evaluate our method by comparing with several baselines in terms of the accuracy.

References

[1]

A. Sheth, P. Anantharam, and C. Henson. Semantic, cognitive, and perceptual computing: Paradigms that shape human experience. Computer, 49(3):64--72, Mar 2016.

Digital Library

[2]

Martin Lindstrom. Broad sensory branding. Journal of Product & Brand Management, 14(2):84--87, 2005.

[3]

K. Pastra, E. Balta, P. Dimitrakis, and G. Karakatsiotis. Embodied language processing: A new generation of language technology. In Proc. of the 14th AAAI Conference on Language-Action Tools for Cognitive Artificial Agents, pages 23--29, 2011.

Digital Library

[4]

George A Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39--41, 1995.

Digital Library

[5]

John Kounios, Jessica I. Fleck, Deborah L. Green, Lisa Payne, Jennifer L. Stevenson, Edward M. Bowden, and Mark Jung-Beeman. The origins of insight in resting-state brain activity. Neuropsychologia, 46(1):281--291, 2008.

[6]

Itamar Simonson. In defense of consciousness: The role of conscious and unconscious inputs in consumer choice. Journal of Consumer Psychology, 15(3):211--217, 2005.

[7]

Charles Spence and Maya U. Shankar. The influence of auditory cues on the perception of, and responses to, food and drink. Journal of Sensory Studies, 25(3):406--430, 2010.

[8]

Bernd H Schmitt. Experiential marketing: How to get customers to sense, feel, think, act, relate. Simon and Schuster, 2000.

[9]

Mariam Adedoyin-Olowe, Mohamed Medhat Gaber, and Frederic T. Stahl. A survey of data mining techniques for social media analysis. CoRR, abs/1312.4617, 2013.

[10]

Peter D. Turney. Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL '02, pages 417--424, Stroudsburg, PA, USA, 2002. Association for Computational Linguistics.

Digital Library

[11]

Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. Thumbs up?: Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10, EMNLP '02, pages 79--86, Stroudsburg, PA, USA, 2002. Association for Computational Linguistics.

Digital Library

[12]

An-Jung Cheng, Yan-Ying Chen, Yen-Ta Huang, Winston H Hsu, and Hong-Yuan Mark Liao. Personalized travel recommendation by mining people attributes from community-contributed photos. In Proceedings of the 19th ACM international conference on Multimedia, pages 83--92. ACM, 2011.

Digital Library

[13]

Johan Bollen and Huina Mao. Twitter mood as a stock market predictor. Computer, 44:91--94, 2011.

Digital Library

[14]

D. Watts, K. M. George, T. K. A. Kumar, and Z. Arora. Tweet sentiment as proxy for political campaign momentum. In 2016 IEEE International Conference on Big Data (Big Data), pages 2475--2484, 2016.

[15]

Jie Yin, Andrew Lampert, Mark Cameron, Bella Robinson, and Robert Power. Using social media to enhance emergency situation awareness. IEEE Intelligent Systems, 27(6):52--59, 2012.

Digital Library

[16]

Sarah Vieweg, Amanda L. Hughes, Kate Starbird, and Leysia Palen. Microblogging during two natural hazards events: What twitter may contribute to situational awareness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '10, pages 1079--1088, New York, NY, USA, 2010. ACM.

Digital Library

[17]

Takeshi Sakaki, Makoto Okazaki, and Yutaka Matsuo. Earthquake shakes twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web, WWW '10, pages 851--860, New York, NY, USA, 2010. ACM.

Digital Library

[18]

Roberto Navigli. Word sense disambiguation: A survey. ACM Comput. Surv., 41(2):10:1--10:69, February 2009.

Digital Library

[19]

Michael Lesk. Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone. In Proceedings of the 5th Annual International Conference on Systems Documentation, SIGDOC '86, pages 24--26, New York, NY, USA, 1986. ACM.

Digital Library

[20]

Kristina Toutanova, Dan Klein, Christopher D. Manning, and Yoram Singer. Feature-rich part-of-speech tagging with a cyclic dependency network. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1, NAACL 03, pages 173--180, Stroudsburg, PA, USA, 2003. Association for Computational Linguistics.

Digital Library

[21]

H. P. Luhn. A statistical approach to mechanized encoding and searching of literary information. IBM Journal of Research and Development, 1(4):309--317, Oct 1957.

Digital Library

[22]

Hwanjo Yu, Jiawei Han, and Kevin Chen-Chuan Chang. Pebl: Positive example based learning for web page classification using svm. In Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '02, pages 239--248, New York, NY, USA, 2002. ACM.

Digital Library

[23]

J. Ross Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993.

Digital Library

[24]

Tin Kam Ho. Random decision forests. In Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on, volume 1, pages 278--282. IEEE, 1995.

Digital Library

[25]

G. Cybenko. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, 2(4):303--314, 1989.

[26]

Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Janvin. A neural probabilistic language model. J. Mach. Learn. Res., 3:1137--1155, March 2003.

Digital Library

[27]

Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernockỳ, and Sanjeev Khudanpur. Recurrent neural network based language model. In Interspeech, volume 2, page 3, 2010.

[28]

Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1):10--18, November 2009.

Digital Library

[29]

T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient Estimation of Word Representations in Vector Space. ArXiv e-prints, January 2013.

[30]

Chih-Chung Chang and Chih-Jen Lin. Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3):27, 2011.

Digital Library

[31]

Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta, may 2010. European Language Resources Association.

Cited By

Lee JJatowt AKim K(2020)Discovering underlying sensations of human emotions based on social mediaJournal of the Association for Information Science and Technology10.1002/asi.24414Online publication date: 29-Sep-2020
https://doi.org/10.1002/asi.24414
Lee JOgawa HKwon YKim K(2018)Spatial Footprints of Human Perceptual Experience in Geo-Social MediaISPRS International Journal of Geo-Information10.3390/ijgi70200717:2(71)Online publication date: 23-Feb-2018
https://doi.org/10.3390/ijgi7020071
Lee JThabsuwan CPongpaichet SKim K(2018)Towards Building a Human Perception Knowledge for Social Sensation Analysis2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2018.00-15(668-671)Online publication date: Dec-2018
https://doi.org/10.1109/WI.2018.00-15

Index Terms

Understanding human perceptual experience in unstructured data on the web
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
      2. Lexical semantics
2. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection
    2. Retrieval tasks and goals
      1. Information extraction

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cover image ACM Conferences

WI '17: Proceedings of the International Conference on Web Intelligence

August 2017

1284 pages

ISBN:9781450349512

DOI:10.1145/3106426

Conference Chair:
Amit Sheth
Wright State University GuoROLE@GENERAL CHAIR
,
General Chairs:
Axel Ngonga
Leipzig University, Germany
,
yin Wang
Chongqing University of Posts and Telecommunications, China
,
Elizabeth Chang
The University of New South Wales, Australia
,
Dominik Ślęzak
Infobright Inc. & University of Warsaw, Poland
,
Bogdan Franczyk
Leipzig University, Germany
,
Program Chairs:
Rainer Alt
Leipzig University, Germany
,
Xiaohui Tao
University of Southern Queensland, Australia

Copyright © 2017 ACM.

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SIGAI: ACM Special Interest Group on Artificial Intelligence
TCII: IEEE Computer Society Technical Committee on Intelligent Informatics
Web Intelligence Consortium

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

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Funding Sources

Japan Society for the Promotion of Science
GRRC program of Gyeonggi province
New Energy and Industrial Technology Development Organization

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WI '17

Sponsor:

SIGAI
TCII

WI '17: International Conference on Web Intelligence 2017

August 23 - 26, 2017

Leipzig, Germany

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;

Overall Acceptance Rate 118 of 178 submissions, 66%

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Cited By

Lee JJatowt AKim K(2020)Discovering underlying sensations of human emotions based on social mediaJournal of the Association for Information Science and Technology10.1002/asi.24414Online publication date: 29-Sep-2020
https://doi.org/10.1002/asi.24414
Lee JOgawa HKwon YKim K(2018)Spatial Footprints of Human Perceptual Experience in Geo-Social MediaISPRS International Journal of Geo-Information10.3390/ijgi70200717:2(71)Online publication date: 23-Feb-2018
https://doi.org/10.3390/ijgi7020071
Lee JThabsuwan CPongpaichet SKim K(2018)Towards Building a Human Perception Knowledge for Social Sensation Analysis2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2018.00-15(668-671)Online publication date: Dec-2018
https://doi.org/10.1109/WI.2018.00-15

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