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Associating Intent with Sentiment in Weblogs

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Natural Language Processing and Information Systems (NLDB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9103))

Abstract

People willingly provide more and more information about themselves on social media platforms. This personal information about users’ emotions (sentiment) or goals (intent) is particularly valuable, for instance, for monitoring tools. So far, sentiment and intent analysis were conducted separately. Yet, both aspects can complement each other thereby informing processes such as explanation and reasoning. In this paper, we investigate the relation between intent and sentiment in weblogs. We therefore extract ~90,000 human goal instances from the ICWSM 2009 Spinn3r dataset and assign respective sentiments. Our results indicate that associating intent with sentiment represents a valuable addition to research areas such as text analytics and text understanding.

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Notes

  1. 1.

    http://www.nltk.org/.

  2. 2.

    The part-of-speech tags used in this paper are consistent with the Penn Treebank Tag Set.

References

  1. Burton, K., Java, A., Soboroff, I.: The ICWSM 2009 spinn3r dataset. In: Proceedings of the 3rd Annual Conference on Weblogs and Social Media (2009)

    Google Scholar 

  2. Carberry, S.: Techniques for plan recognition. J. User Model. User-Adap. Inter. 11(1–2), 31–48 (2001)

    Article  MATH  Google Scholar 

  3. De Choudhury, M., Sundaram, H., John, A., Seligmann, D.: Can blog communication dynamics be correlated with stock market activity?. In: Proceedings of the 19th ACM Conference on Hypertext and Hypermedia (2008)

    Google Scholar 

  4. Faaborg, A., Lieberman, H.: A goal-oriented web browser. In: Proceedings of the Conference on Human Factors in Computing Systems (2006)

    Google Scholar 

  5. Hearst, M.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics (1992)

    Google Scholar 

  6. Kroha, P., Baeza-Yates, R., Krellner, B.: Text mining of business news for forecasting. In: The International Workshop on Database and Expert Systems Applications (2006)

    Google Scholar 

  7. Kröll, M., Strohmaier, M.: Analyzing human intentions in natural language text. In: Proceedings of the 5th International Conference on Knowledge Capture (2009)

    Google Scholar 

  8. Levin, B.: English Verb Classes and Alternations: A Preliminary Investigation. University of Chicago Press, Chicago (1993)

    Google Scholar 

  9. Lenat, D.: CYC: a large-scale investment in knowledge infrastructure. Commun. ACM 38(11), 33–38 (1995)

    Article  Google Scholar 

  10. Lieberman, H.: Usable AI requires common sense knowledge. In: Workshops and Courses: Usable Artificial Intelligence held in Conjunction with CHI 2008 (2008)

    Google Scholar 

  11. Lietz, H., Wagner, C., Bleier, A., Strohmaier, M.: When politicians talk: assessing online conversational practices of political parties on twitter. In: International AAAI Conference on Weblogs and Social Media (2014)

    Google Scholar 

  12. Liu, H., Singh, P.: ConceptNet - a practical commonsense reasoning tool-kit. BT Technol. J. 22(4), 211–226 (2004)

    Article  MathSciNet  Google Scholar 

  13. Liu, H., Lieberman, H., Selker, T.: GOOSE: a goal-oriented search engine with commonsense. In: Proceedings of the 2nd International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2002)

    Google Scholar 

  14. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2002)

    Google Scholar 

  15. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  16. Regev, G., Wegmann, A.: Where do goals come from: the underlying principles of goal-oriented requirements engineering. In: Proceedings of the 13th International Conference on Requirements Engineering (2005)

    Google Scholar 

  17. Schank, R., Abelson, R.: Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Lawrence Erlbaum Associates, Hillsdale (1977)

    MATH  Google Scholar 

  18. Smith, D.: EventMinder: a personal calendar assistant that understands events. Master’s Thesis at the Massachusetts Institute of Technology (2007)

    Google Scholar 

  19. Smith, D., Lieberman, H.: The why UI: using goal networks to improve user interfaces. In: Proceedings of the 14th International Conference on Intelligent User Interfaces (2010)

    Google Scholar 

  20. Stone, P., Dunphy, D., Smith, M., Ogilvie, D.: The General Inquirer: A Computer Approach to Content Analysis. M.I.T. Press, Cambridge, Mass (1966)

    Google Scholar 

  21. Strohmaier, M., Prettenhofer, P., Kröll, M.: Acquiring explicit user goals from search query logs. In: International Workshop on Agents and Data Mining Interaction (2008)

    Google Scholar 

  22. Turney, P.D.: 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 (2002)

    Google Scholar 

  23. Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 1(2), 165–210 (2005)

    Article  Google Scholar 

  24. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of Human Language Technologies Conference/Conference on Empirical Methods in Natural Language Processing (2005)

    Google Scholar 

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Acknowledgments

Thanks to Daniel Lamprecht and Johannes Liegl for participating in this work. This work is funded by the KIRAS program of the Austrian Research Promotion Agency (FFG) (project number 840824). The Know-Center is funded within the Austrian COMET Program under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labor and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG.

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Correspondence to Mark Kröll .

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Kröll, M., Strohmaier, M. (2015). Associating Intent with Sentiment in Weblogs. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_19

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  • DOI: https://doi.org/10.1007/978-3-319-19581-0_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19580-3

  • Online ISBN: 978-3-319-19581-0

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