Event-Based Recognition of Lived Experiences in User Reviews

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10024)

Abstract

User reviews on the web are an important source of opinions on products and services. For a popular product or service, the number of reviews can be large. Therefore, it may be difficult for a potential customer to read all of them and make a decision. We hypothesize and test if lived experiences from reviews may support the confidence of a user in a review. We identify and extract such lived experiences with a novel technique based on machine reading. Our experimental results demonstrate the effectiveness of the technique.

Keywords

Lived experiences extraction Event extraction Machine reading Semantic web User reviews 

References

  1. 1.
    Aone, C., Ramos-Santacruz, M.: REES: a large-scale relation and event extraction system. In: Proceedings of the Sixth Conference on Applied Natural Language Processing, pp. 76–83 (2000)Google Scholar
  2. 2.
    Barsalou, L.W.: The content and organization of autobiographical memories. In: Remembering Reconsidered: Ecological and Traditional Approaches to the Study of Memory, pp. 193–243 (1988)Google Scholar
  3. 3.
    Baumgartner, H., Sujan, M., Bettman, J.R.: Autobiographical memories, affect, and consumer information processing. J. Consum. Psychol. 1, 53–82 (1992)CrossRefGoogle Scholar
  4. 4.
    Bethard, S., Martin, J.H.: Identification of event mentions and their semantic class. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 146–154 (2006)Google Scholar
  5. 5.
    Duffy, A.M.: Fellow travellers: what do users trust on recommender websites? A case study of TripAdvisor.com. Ph.D. thesis (2012)
  6. 6.
    Eastmond, M.: Stories as lived experience: narratives in forced migration research. J. Refugee Stud. 20, 248–264 (2007)CrossRefGoogle Scholar
  7. 7.
    Etzioni, O., Banko, M., Cafarella, M.: Machine reading. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI) (2006)Google Scholar
  8. 8.
    Gangemi, A.: A comparison of knowledge extraction tools for the semantic web. In: Cimiano, P., Corcho, O., Presutti, V., Hollink, L., Rudolph, S. (eds.) ESWC 2013. LNCS, vol. 7882, pp. 351–366. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Gangemi, A., Hassan, E., Presutti, V., Reforgiato, D.: Fred as an event extraction tool. In: Proceedings of the Workshop on Detection, Representation, and Exploitation of Events in the Semantic Web, p. 14 (2013)Google Scholar
  10. 10.
    Gangemi, A., Presutti, V., Recupero, D.R., Nuzzolese, A.G., Draicchio, F., Mongiovi, M.: Semantic web machine reading with FRED. In: Sermantic Web (2016). http://www.semantic-web-journal.net/system/files/swj1297.pdf
  11. 11.
    Gordon, A., Swanson, R.: Identifying personal stories in millions of weblog entries. In: Third International Conference on Weblogs and Social Media, Data Challenge Workshop, San Jose, CA (2009)Google Scholar
  12. 12.
    Gordon, A.S.: Story management technologies for organizational learning. In: International Conference on Knowledge Management. In Special Track on Intelligent Assistance for Self-directed and Organizational Learning, Graz, Austria (2008)Google Scholar
  13. 13.
    Hassan, E., Buscaldi, D., Gangemi, A.: Correlating open rating systems and event extraction from text. In: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9492, pp. 367–375. Springer, Heidelberg (2015). doi:10.1007/978-3-319-26561-2_44 CrossRefGoogle Scholar
  14. 14.
    Inui, K., Abe, S., Hara, K., Morita, H., Sao, C., Eguchi, M., Sumida, A., Murakami, K., Matsuyoshi, S.: Experience mining: building a large-scale database of personal experiences and opinions from web documents. In: Proceedings of the 2008 International Conference on Web Intelligence and Intelligent Agent Technology. IEEE Computer Society, Washington, DC (2008)Google Scholar
  15. 15.
    Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 219–230. ACM (2008)Google Scholar
  16. 16.
    Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Vol. 1, pp. 309–319 (2011)Google Scholar
  18. 18.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Ploeger, T., Kruijt, M., Aroyo, L., de Bakker, F., Hellsten, I., Fokkens, A., Hoeksema, J., ter Braake, S.: Extracting activist events from news articles using existing NLP tools and services. In: Proceedings of the Workshop on Detection, Representation, and Exploitation of Events in the Semantic Web, p. 30 (2013)Google Scholar
  20. 20.
    Presutti, V., Draicchio, F., Gangemi, A.: Knowledge Extraction based on discourse representation theory and linguistic frames. In: ten Teije, A., Völker, J., Handschuh, S., Stuckenschmidt, H., d’Acquin, M., Nikolov, A., Aussenac-Gilles, N., Hernandez, N. (eds.) EKAW 2012. LNCS, vol. 7603, pp. 114–129. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Ritter, A., Etzioni, O., Clark, S., et al.: Open domain event extraction from twitter. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1104–1112 (2012)Google Scholar
  22. 22.
    Saurí, R., Knippen, R., Verhagen, M., Pustejovsky, J.: Evita: a robust event recognizer for QA systems. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 700–707 (2005)Google Scholar
  23. 23.
    Treloar, C., Rhodes, T.: The lived experience of hepatitis c and its treatment among injecting drug users: qualitative synthesis. Qual. Health Res. 19, 1321–1334 (2009)CrossRefGoogle Scholar
  24. 24.
    Tung, V.W.S., Ritchie, J.B.: Exploring the essence of memorable tourism experiences. Ann. Tourism Res. 38, 1367–1386 (2011)CrossRefGoogle Scholar
  25. 25.
    Van Oorschot, G., Van Erp, M., Dijkshoorn, C.: Automatic extraction of soccer game events from twitter. In: Proceedings of the Workshop on Detection, Representation, and Exploitation of Events in the Semantic Web, p. 15 (2012)Google Scholar
  26. 26.
    Zhang, Y., Xu, C., Rui, Y., Wang, J., Lu, H.: Semantic event extraction from basketball games using multi-modal analysis. In: 2007 IEEE International Conference on Multimedia and Expo, pp. 2190–2193 (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.LIPN, Université Paris XIII, CNRS UMR 7030VilletaneuseFrance
  2. 2.STLab, ISTC-CNRRomeItaly

Personalised recommendations