Advertisement

Background

  • Erik Cambria
  • Amir Hussain
Chapter
Part of the SpringerBriefs in Cognitive Computation book series (BRIEFSCC, volume 2)

Abstract

The World Wide Web represents one of the most revolutionary applications in the history of computing and human communication, which is keeping on changing how information is disseminated and retrieved, how business is conducted and how people communicate with each other. As the dimension of the Web increases, the technologies used in its development and the services provided to its users are developing constantly. Even if just few years have passed, in fact, Web 1.0’s static and read-only HTML pages seem now just an old memory. Today the Web has become a dynamic and interactive reality in which more and more people actively participate by creating, sharing, and consuming contents. In this way, the World Wide Web configures itself not only as a ‘Web of data’, but also as a ‘Web of people’ where data and users are interconnected in an unbreakable bond.

Keywords

Common Sense Resource Description Framework Opinion Mining Sentiment Analysis Structure Query Language 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Salzman, M., Matathia, I., O’Reilly, A.: Buzz: Harness the Power of Influence and Create Demand. Wiley, New York (2003)Google Scholar
  2. 2.
    Cesarano, C., Dorr, B., Picariello, A., Reforgiato, D., Sagoff, A., Subrahmanian, V.: OASYS: An opinion analysis system. AAAI CAAW. Stanford, In (2006)Google Scholar
  3. 3.
    Sood, S., Vasserman, L.: ESSE: exploring mood on the web. ICWSM. San Jose, In (2009)Google Scholar
  4. 4.
    Speer, R., Havasi, C., Treadway, N., Lieberman, H.: Finding your way in a multi-dimensional semantic space with Luminoso. IUI. Hong Kong, In (2010)Google Scholar
  5. 5.
    Dave, K., Lawrence, S., Pennock, D.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. WWW. Budapest, In (2003)Google Scholar
  6. 6.
    Das, S., Chen, M.: Yahoo! for Amazon: extracting market sentiment from stock message boards. APFA. Bangkok, In (2001)Google Scholar
  7. 7.
    Tong, R.: An operational system for detecting and tracking opinions in on-line discussion. SIGIR. New Orleans, In (2001)Google Scholar
  8. 8.
    Hsinchun, C., Zimbra, D.: AI and opinion mining. IEEE Intell. Syst. 25(3), 74–80 (2010)CrossRefGoogle Scholar
  9. 9.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2, 1–135 (2008)CrossRefGoogle Scholar
  10. 10.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: EMNLP, pp. 79–86. Philadelphia (2002).Google Scholar
  11. 11.
    Mihalcea, R., Banea, C., Wiebe, J.: Learning multilingual subjective language via cross-lingual projections. ACL. Prague, In (2007)Google Scholar
  12. 12.
    Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? Finding strong and weak opinion clauses. In: AAAI, pp. 761–769. San Jose (2004).Google Scholar
  13. 13.
    Hatzivassiloglou, V., Wiebe, J.: Effects of adjective orientation and gradability on sentence subjectivity. COLING. Saarbrücken, In (2000)Google Scholar
  14. 14.
    Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning subjective language. Comput. Linguist. 30(3) (2004).Google Scholar
  15. 15.
    Riloff, E., Wiebe, J., Phillips, W.: Exploiting subjectivity classification to improve information extraction. AAAI. Pittsburgh, In (2005)Google Scholar
  16. 16.
    Finn, A., Kushmerick, N.: Learning to classify documents according to genre. J. Am. Soc. Inf. Sci. Technol. 7(5) (2006).Google Scholar
  17. 17.
    Biber, D.: Variation across Speech and Writing. Cambridge University Press, Cambridge (1988)CrossRefGoogle Scholar
  18. 18.
    Mosteller, F., Wallace, D.: Applied Bayesian and Classical Inference: The Case of the Federalist Papers. Springer, Berlin (1984)CrossRefGoogle Scholar
  19. 19.
    Argamon, S., Koppel, M., Avneri, G.: Style-based text categorization: What newspaper am i reading?. AAAI Workshop on Text Categorization. Madison, In (1998)Google Scholar
  20. 20.
    Cambria, E., Hussain, A., Durrani, T., Wang, Q.: Towards a chinese common and common sense knowledge base for sentiment analysis. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds.) Advanced Research in Applied Artificial Intelligence, Lecture Notes in Artificial Intelligence, vol. 7345, pp. 437–446. Springer, Berlin (2012).Google Scholar
  21. 21.
    Hatzivassiloglou, V., McKeown, K.: Predicting the semantic orientation of adjectives. In: ACL/EACL. Madrid (1997).Google Scholar
  22. 22.
    Popescu, A., Etzioni, O.: Extracting product features and opinions from reviews. In: HLT/EMNLP. Vancouver (2005).Google Scholar
  23. 23.
    Snyder, B., Barzilay, R.: Multiple aspect ranking using the good grief algorithm. In: HLT/NAACL. Rochester (2007).Google Scholar
  24. 24.
    Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: ACL, pp. 271–278. Barcelona (2004).Google Scholar
  25. 25.
    Joshi, M., Rose, C.: Generalizing dependency features for opinion mining. ACL/IJCNLP. Singapore, In (2009)Google Scholar
  26. 26.
    Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: ACL, pp. 115–124. Ann, Arbor (2005).Google Scholar
  27. 27.
    Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: ACL, pp. 417–424. Philadelphia (2002).Google Scholar
  28. 28.
    Kamps, J., Marx, M., Mokken, R., de Rijke, M.: Using WordNet to measure semantic orientation of adjectives. In: LREC, pp. 1115–1118. Lisbon (2004).Google Scholar
  29. 29.
    Kim, S., Hovy, E.: Automatic detection of opinion bearing words and sentences. In: IJCNLP, pp. 61–66. Jeju Island (2005).Google Scholar
  30. 30.
    Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: EMNLP, pp. 105–112. Sapporo (2003).Google Scholar
  31. 31.
    Baker, C., Fillmore, C., Lowe, J.: The Berkeley FrameNet project. In: COLING/ACL, pp. 86–90. Montreal (1998).Google Scholar
  32. 32.
    Kim, S., Hovy, E.: Extracting opinions, opinion holders, and topics expressed in online news media text. Workshop on Sentiment and Subjectivity in Text. Sydney, In (2006)Google Scholar
  33. 33.
    Agrawal, R., Srikant, R.: Fast algorithm for mining association rules. VLDB. Santiago de Chile, In (1994)Google Scholar
  34. 34.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: KDD, Seattle (2004)Google Scholar
  35. 35.
    Zirn, C., Niepert, M., Stuckenschmidt, H., Strube, M.: Fine-grained sentiment analysis with structural features. IJCNLP. Chiang Mai, In (2011)Google Scholar
  36. 36.
    Elliott, C.D.: The affective reasoner: a process model of emotions in a multi-agent system. Ph.D. thesis, Northwestern University, Evanston (1992).Google Scholar
  37. 37.
    Ortony, A., Clore, G., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, Cambridge (1988)CrossRefGoogle Scholar
  38. 38.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 39(2), 165–210 (2005)CrossRefGoogle Scholar
  39. 39.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. HLT/EMNLP. Vancouver, In (2005)Google Scholar
  40. 40.
    Stevenson, R., Mikels, J., James, T.: Characterization of the affective norms for english words by discrete emotional categories. Behav. Res. Methods 39, 1020–1024 (2007)PubMedCrossRefGoogle Scholar
  41. 41.
    Somasundaran, S., Wiebe, J., Ruppenhofer, J.: Discourse level opinion interpretation. In: COLING. Manchester (2008).Google Scholar
  42. 42.
    Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: EACL, pp. 675–682. Athens (2009).Google Scholar
  43. 43.
    Goertzel, B., Silverman, K., Hartley, C., Bugaj, S., Ross, M.: The Baby Webmind project. In: AISB. Birmingham (2000).Google Scholar
  44. 44.
    Turney, P., Littman, M.: Measuring praise and criticism: inference of semantic orientation from association. ACM Trans. Inf. Syst. 21(4), 315–346 (2003)CrossRefGoogle Scholar
  45. 45.
    Abbasi, A., Chen, H., Salem, A.: Sentiment analysis in multiple languages: feature selection for opinion classification in web forums. ACM Trans. Inf. Syst. 26(3), 1–34 (2008)CrossRefGoogle Scholar
  46. 46.
    Nguyen, L., Wu, P., Chan, W., Peng, W., Zhang, Y.: Predicting collective sentiment dynamics from time-series social media. In: ACM KDD WISDOM, Beijing (2012)Google Scholar
  47. 47.
    Di Fabbrizio, G., Aker, A., Gaizauskas, R.: Starlet: Multi-document summarization of service and product reviews with balanced rating distributions. In: IEEE ICDM SENTIRE, Vancouver (2011)Google Scholar
  48. 48.
    Velikovich, L., Goldensohn, S., Hannan, K., McDonald, R.: The viability of web-derived polarity lexicons. In: NAACL, pp. 777–785. Los Angeles (2010).Google Scholar
  49. 49.
    Minsky, M.: The Society of Mind. Simon and Schuster, New York (1986)Google Scholar
  50. 50.
    Cambria, E., Hussain, A., Havasi, C., Eckl, C.: Common sense computing: from the society of mind to digital intuition and beyond. In: Fierrez, J., Ortega, J., Esposito, A., Drygajlo, A., Faundez-Zanuy, M. (eds.) Biometric ID Management and Multimodal Communication, Lecture Notes in Computer Science, p. 259. Springer, Berlin (2009).Google Scholar
  51. 51.
    Murphy, G.: The Big Book of Concepts. The MIT Press, Cambridge (2004)Google Scholar
  52. 52.
    Bloom, P.: Glue for the mental world. Nature 421, 212–213 (2003)CrossRefGoogle Scholar
  53. 53.
    Barwise, J.: An introduction to first-order logic. Handbook of Mathematical Logic. In: Studies in Logic and the Foundations of Mathematics. North-Holland (1977).Google Scholar
  54. 54.
    Reiter, R.: A logic for default reasoning. Artif. Intell. 13, 81–132 (1980)CrossRefGoogle Scholar
  55. 55.
    Heyting, A.: Intuitionism. An introduction North-Holland (1956).Google Scholar
  56. 56.
    Date, C., Darwen, H.: A Guide to the SQL Standard. Addison-Wesley, Reading (1993).Google Scholar
  57. 57.
    Codd, E.: A relational model of data for large shared data banks. Commun. ACM 13(6), 377–387 (1970)CrossRefGoogle Scholar
  58. 58.
    Codd, E.: Further normalization of the data base relational model. Technical Report, IBM Research Report, New York (1971)Google Scholar
  59. 59.
    Codd, E.: Recent investigations into relational data base systems. Technical Report RJ1385, IBM Research Report, New York (1974).Google Scholar
  60. 60.
    Chomsky, N.: Three models for the description of language. IRE Trans. Inf. Theory 2(3), 113–124 (1956)CrossRefGoogle Scholar
  61. 61.
    Lacy, L.: OWL: Representing Information Using the Web Ontology Language. Trafford Publishing, Victoria (2005).Google Scholar
  62. 62.
    Pearl, J.: Bayesian networks: a model of self-activated memory for evidential reasoning. Technical Report CSD-850017, UCLA Technical Report, Irvine (1985).Google Scholar
  63. 63.
    Sowa, J.: Semantic networks. Encyclopedia of Artificial Intelligence. Stuart Shapiro, In (1987)Google Scholar
  64. 64.
    Winston, P.: Learning structural descriptions from examples. The Psychology of Computer Vision. pp. 157–209. McGraw-Hill, New York (1975).Google Scholar
  65. 65.
    McCarthy, J.: Programs with common sense. Teddington Conference on the Mechanization of Thought Processes, In (1959)Google Scholar
  66. 66.
    Ernest, D.: Representations of Commonsense Knowledge. Morgan Kaufmann, San Francisco (1990)Google Scholar
  67. 67.
    Lenat, D., Guha, R.: Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project. Addison-Wesley, Boston (1989)Google Scholar
  68. 68.
    Fellbaum, C.: WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press, Cambridge (1998)Google Scholar
  69. 69.
    Mueller, E.: Natural Language Processing with ThoughtTreasure. Signifonn, New York (1998)Google Scholar
  70. 70.
    Minsky, M.: Commonsense-based interfaces. Commun. ACM 43(8), 67–73 (2000)CrossRefGoogle Scholar
  71. 71.
    Singh, P.: The open mind common sense project. KurzweilAI.net (2002).Google Scholar
  72. 72.
    Stork, D.: The open mind initiative. IEEE Intell. Syst. 14(3), 16–20 (1999)CrossRefGoogle Scholar
  73. 73.
    Chklovski, T.: Learner: a system for acquiring commonsense knowledge by analogy. In: K-CAP (2003).Google Scholar
  74. 74.
    Speer, R., Havasi, C., Lieberman, H.: Analogyspace: reducing the dimensionality of common sense knowledge. AAAI, In (2008)Google Scholar
  75. 75.
    Havasi, C., Speer, R., Pustejovsky, J., Lieberman, H.: Digital intuition: applying common sense using dimensionality reduction. IEEE Intell. Syst. 24(4), 24–35 (2009)CrossRefGoogle Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  1. 1.Media LaboratoryMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Department of Computing ScienceUniversity of StirlingStirlingUK

Personalised recommendations