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Social Media Retrieval pp 191–215Cite as

Sentic Computing for Social Media Analysis, Representation, and Retrieval

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Abstract

As the web is rapidly evolving, web users are evolving with it. In the era of social colonisation, people are getting more and more enthusiastic about interacting, sharing and collaborating through social networks, online communities, blogs, wikis and other online collaborative media. In recent years, this collective intelligence has spread to many different areas in the web, with particular focus on fields related to our everyday life such as commerce, tourism, education, and health. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. To overcome such obstacle, we need to explore more concept-level approaches that rely more on the implicit semantic texture of natural language, rather than its explicit syntactic structure. To this end, we further develop and apply sentic computing tools and techniques to the development of a novel unified framework for social media analysis, representation and retrieval. The proposed system extracts semantics from natural language text by applying graph mining and multidimensionality reduction techniques on an affective common sense knowledge base and makes use of them for inferring the cognitive and affective information associated with social media.

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References

  1. Minsky, M.: The Emotion Machine: Commonsense Thinking, Artificial Intelligence, and the Future of the Human Mind. Simon and Schuster, New York (2006)

    Google Scholar 

  2. Cambria, E., Hussain, A.: Sentic Computing: Techniques, Tools, and Applications. Springer, Dordrecht (2012)

    Google Scholar 

  3. Charles, D.: The Expression of the Emotions in Man and Animals. John Murray, London (1872)

    Google Scholar 

  4. James, W.: What is an emotion? Mind 34, 188–205 (1884)

    Article  Google Scholar 

  5. Osgood, C., May, W., Miron, M.: Cross-cultural universals of affective meaning. University of Illinois, Urbana (1975)

    Google Scholar 

  6. Lutz, C., White, G.: The anthropology of emotions. Ann. Rev. Anthropol. 15, 405–436 (1986)

    Article  Google Scholar 

  7. Turkle, S.: The Second Self: Computers and the Human Spirit. Simon and Schuster, New York (1984)

    Google Scholar 

  8. Scherer, K.: Studying the emotion-antecedent appraisal process: an expert system approach. Cognit. Emot. 7, 325–355 (1993)

    Article  Google Scholar 

  9. Picard, R.: Affective computing. MIT, Boston (1997)

    Google Scholar 

  10. Cambria, E., Hupont, I., Hussain, A., Cerezo, E., Baldassarri, S.: Sentic avatar: multimodal affective conversational agent with common sense. In: Esposito, A., Hussain, A., Faundez-Zanuy, M., Martone, R., Melone, N. (eds.) Toward Autonomous, Adaptive, and Context-Aware Multimodal Interfaces: Theoretical and Practical Issues. Lecture Notes in Computer Science, vol. 6456, pp. 82–96. Springer, Berlin/Heidelberg (2011)

    Google Scholar 

  11. Cambria, E., Olsher, D., Kwok, K.: Sentic activation: a two-level affective common sense reasoning framework. In: Proceedings of the AAAI, Toronto pp. 186–192 (2012)

    Google Scholar 

  12. Cambria, E., Olsher, D., Kwok, K.: Sentic panalogy: swapping affective common sense reasoning strategies and foci. In: Proceedings of the CogSci, Sapporo, pp. 174–179 (2012)

    Google Scholar 

  13. Cambria, E., Hussain, A., Havasi, C., Eckl, C.: SenticSpace: visualizing opinions and sentiments in a multi-dimensional vector space. In: Setchi, R., Jordanov, I., Howlett, R., Jain, L. (eds.) Knowledge-Based and Intelligent Information and Engineering Systems. Lecture Notes in Artificial Intelligence, vol. 6279, pp. 385–393. Springer, Berlin (2010)

    Google Scholar 

  14. Liu, H., Singh, P.: ConceptNet: a practical commonsense reasoning toolkit. BT Technol. J. (2004)

    Google Scholar 

  15. Lenat, D., Guha, R.: Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Addison-Wesley, Boston (1989)

    Google Scholar 

  16. Mueller, E.: Commonsense Reasoning. Morgan Kaufmann, Amsterdam/Boston (2006)

    Google Scholar 

  17. Havasi, C., Speer, R., Alonso, J.: ConceptNet 3: a flexible, multilingual semantic network for common sense knowledge. In: Proceedings of the RANLP, Borovets (2007)

    Google Scholar 

  18. Speer, R.: Open mind commons: an inquisitive approach to learning common sense. In: Proceedings of the Workshop on Common Sense and Interactive Applications, Honolulu (2007)

    Google Scholar 

  19. Cambria, E., Hussain, A., Durrani, T., Havasi, C., Eckl, C., Munro, J.: Sentic computing for patient centered application. In: Proceedings of the IEEE ICSP, Beijing, pp. 1279–1282 (2010)

    Google Scholar 

  20. Havasi, C., Speer, R., Holmgren, J.: Automated color selection using semantic knowledge. In: Proceedings of the AAAI CSK, Arlington (2010)

    Google Scholar 

  21. Havasi, C., Speer, R., Pustejovsky, J., Lieberman, H.: Digital intuition: applying common sense using dimensionality reduction. IEEE Intell. Syst. 24(4), 24–35 (2009)

    Article  Google Scholar 

  22. Strapparava, C., Valitutti, A.: WordNet-affect: an affective extension of WordNet. In: Proceedings of the LREC, Lisbon (2004)

    Google Scholar 

  23. Wall, M., Rechtsteiner, A., Rocha, L.: Singular value decomposition and principal component analysis. In: Berrar, D., Dubitzky, W., Granzow, M. (eds.) A Practical Approach to Microarray Data Analysis, pp. 91–109. Kluwer Academic Publishers, Boston (2003)

    Chapter  Google Scholar 

  24. Eckart, C., Young, G.: The approximation of one matrix by another of lower rank. Psychometrika 1(3), 211–218 (1936)

    Article  MATH  Google Scholar 

  25. Plutchik, R.: The nature of emotions. Am. Sci. 89(4), 344–350 (2001)

    Google Scholar 

  26. Cambria, E., Mazzocco, T., Hussain, A., Eckl, C.: Sentic medoids: organizing affective common sense knowledge in a multi-dimensional vector space. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds.) Advances in Neural Networks. Lecture Notes in Computer Science, vol. 6677, pp. 601–610. Springer, Berlin (2011)

    Google Scholar 

  27. Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)

    Book  Google Scholar 

  28. Hartigan, J., Wong, M.: Algorithm AS 136: a k-means clustering algorithm. J. R. Stat. Soc. 28(1), 100–108 (1979)

    MATH  Google Scholar 

  29. Park, H., Jun, C.: A simple and fast algorithm for k-medoids clustering. Expert Syst. Appl. 36(2), 3336–3341 (2009)

    Article  Google Scholar 

  30. Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)

    MATH  Google Scholar 

  31. Cambria, E., Grassi, M., Hussain, A., Havasi, C.: Sentic computing for social media marketing. Multimed. Tools Appl. 59(2), 557–577 (2012)

    Article  Google Scholar 

  32. Lin, W., Wilson, T., Wiebe, J., Hauptmann, A.: Which side are you on? Identifying perspectives at the document and sentence levels. In: Proceedings of the Conference on Natural Language Learning, New York, pp. 109–116 (2006)

    Google Scholar 

  33. D’Mello, S., Craig, S., Sullins, J., Graesser, A.: Predicting affective states expressed through an emote-aloud procedure from autotutor’s mixed-initiative dialogue. Int. J. Artif. Intell. Educ. 16, 3–28 (2006)

    Google Scholar 

  34. D’Mello, S., Dowell, N., Graesser, A.: Cohesion relationships in tutorial dialogue as predictors of affective states. In: Proceedings of the Conference Artificial Intelligence in Education, pp. 9–16. Springer, New York (2009)

    Google Scholar 

  35. Danisman, T., Alpkocak, A.: Feeler: emotion classification of text using vector space model. In: Proceedings of the AISB, Aberdeen (2008)

    Google Scholar 

  36. Strapparava, C., Mihalcea, R.: Learning to identify emotions in text. In: Proceedings of the ACM Symposium Applied Computing, pp. 1556–1560. ACM, New York (2008)

    Google Scholar 

  37. Ma, C., Osherenko, A., Prendinger, H., Ishizuka, M.: A chat system based on emotion estimation from text and embodied conversational messengers. In: Proceedings of the International Conference Active Media Technology, pp. 546–548. IEEE, Piscataway (2005)

    Google Scholar 

  38. Alm, C., Roth, D., Sproat, R.: Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of the HLT/EMNLP, pp. 347–354. Association for Computing Linguistics, Morristown (2005)

    Google Scholar 

  39. Grassi, M., Cambria, E., Hussain, A., Piazza, F.: Sentic web: a new paradigm for managing social media affective information. Cognit. Comput. 3(3), 480–489 (2011)

    Article  Google Scholar 

  40. Cambria, E., Benson, T., Eckl, C., Hussain, A.: Sentic PROMs: application of sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Syst. Appl. 39(12), 10533–10543 (2012)

    Article  Google Scholar 

  41. Cambria, E., Song, Y., Wang, H., Hussain, A.: Isanette: a common and common sense knowledge base for opinion mining. In: Proceedings of the ICDM, Vancouver, pp. 315–322 (2011)

    Google Scholar 

  42. Havasi, C., Speer, R., Pustejovsky, J.: Coarse word-sense disambiguation using common sense. In: Proceedings of the AAAI CSK, Arlington (2010)

    Google Scholar 

  43. Grassi, M.: Developing HEO human emotions ontology. Lecture Notes in Computer Science, vol. 5707, pp. 244–251. Springer, Berlin/Heidelberg (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Temasek Laboratories, National University of Singapore, Singapore, 117411, Singapore

    Erik Cambria

  2. Department of Information Engineering, Universitá Politecnica delle Marche, Ancona, 60131, Italy

    Marco Grassi

  3. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700 032, India

    Soujanya Poria

  4. Department of Computing Science and Mathematics, University of Stirling, Scotland, FK9 4LA, UK

    Amir Hussain

Authors
  1. Erik Cambria
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  2. Marco Grassi
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  3. Soujanya Poria
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  4. Amir Hussain
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Corresponding author

Correspondence to Erik Cambria .

Editor information

Editors and Affiliations

  1. Department of Electronic Engineering, Queen Mary University of London, Mile End Road, London, E1 4NS, United Kingdom

    Naeem Ramzan

  2. Director of Product Innovation, Search, Netflix, Winchester Circle 100, Los Gatos, 95032, California, USA

    Roelof van Zwol

  3. School of Integrated Technology, Yonsei University, 162-1 Songdo-dong, Yeonsu-gu, Incheon, 406-840, Korea, Republic of (South Korea)

    Jong-Seok Lee

  4. Institut für Telekommunikationssysteme, Technische Universität Berlin, Einsteinufer 17, Berlin, 10587, Germany

    Kai Clüver

  5. Media Computing Group, Microsoft Research, 555 108th Ave NE, Bellevue, 98004, Washington, USA

    Xian-Sheng Hua

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Cambria, E., Grassi, M., Poria, S., Hussain, A. (2013). Sentic Computing for Social Media Analysis, Representation, and Retrieval. In: Ramzan, N., van Zwol, R., Lee, JS., Clüver, K., Hua, XS. (eds) Social Media Retrieval. Computer Communications and Networks. Springer, London. https://doi.org/10.1007/978-1-4471-4555-4_9

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  • DOI: https://doi.org/10.1007/978-1-4471-4555-4_9

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  • Print ISBN: 978-1-4471-4554-7

  • Online ISBN: 978-1-4471-4555-4

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