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Competition Detection from Online News

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Knowledge Management and Acquisition for Intelligent Systems (PKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9806))

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Abstract

In this paper, we define a novel problem named competed intention identification of online news. We propose new features to represent the competed intention of the documents. The support vector machine (SVM) is employed to adopt our features to identify the competed intention in the news article. Experimental results demonstrate that the features we designed are effective for identifying the documents with competed intention.

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Notes

  1. 1.

    https://wordnet.princeton.edu/.

  2. 2.

    http://nlp.stanford.edu/software/stanford-dependencies.shtml.

References

  1. Abu-Jbara, A., Diab, M., Dasigi, P., Radev, D.: Subgroup detection in ideological discussions. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012, pp. 399–409 (2012)

    Google Scholar 

  2. Bao, S., Li, R., Yu, Y., Cao, Y.: Competitor mining with the web. IEEE Trans. Knowl. Data Eng. 20, 1297–1310 (2008)

    Article  Google Scholar 

  3. Bengtsson, M., Kock, S.: Coopetition in business networks—to cooperate and compete simultaneously. Ind. Mark. Manage. 29(5), 411–426 (2000)

    Article  Google Scholar 

  4. Bengtsson, M., Kock, S.: Cooperation and competition in relationships between competitors in business networks. J. Bus. Ind. Mark. 14(3), 178–194 (1999)

    Article  Google Scholar 

  5. Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011)

    Article  Google Scholar 

  6. Chase, J.M., Abrams, P.A., Grover, J.P., Diehl, S., Chesson, P., Holt, R.D., Richards, S.A., Nisbet, R.M., Case, T.J.: The interaction between predation and competition: a review and synthesis. Ecol. Lett. 5, 302–315 (2002)

    Article  Google Scholar 

  7. Chen, C.C., Wu, C.-Y.: Bipolar person name identification of topic documents using principal component analysis. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 170–178 (2010)

    Google Scholar 

  8. Chen, C.C., Chen, Z.-Y., Wu, C.-Y.: An unsupervised approach for person name bipolarization using principal component analysis. IEEE Trans. Knowl. Data Eng. 24, 1963–1976 (2012)

    Article  Google Scholar 

  9. Chen, Z., Liu, B., Hsu, M., Castellanos, M., Ghosh, R.: Identifying intention posts in discussion forums. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1041–1050 (2013)

    Google Scholar 

  10. Donders, M.W.M., Vorst, T.C.F.: The impact of firm specific news on implied volatilities. J. Banking Finan. 20(9), 1447–1461 (1996)

    Article  Google Scholar 

  11. Fung, G.P.C., Yu, J.X., Lam, W.: News sensitive stock trend prediction. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 481–493. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  12. Geva, T., Zahavi, J.: Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news. Decis. Support Syst. 57, 212–223 (2014)

    Article  Google Scholar 

  13. Groth, S.S., Siering, M., Gomber, P.: How to enable automated trading engines to cope with news-related liquidity shocks? Extracting signals from unstructured data. Decis. Support Syst. 62, 32–42 (2014)

    Article  Google Scholar 

  14. Hagenau, M., Liebmann, M., Neumann, D.: Automated news reading: stock price prediction based on financial news using context-capturing features. Decis. Support Syst. 55(3), 685–697 (2013)

    Article  Google Scholar 

  15. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2011)

    MATH  Google Scholar 

  16. Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics, pp. 174–181 (1997)

    Google Scholar 

  17. Ma, Z., Sheng, O.R.L., Pant, G.: Discovering company revenue relations from news: a network approach. Decis. Support Syst. 47(4), 408–414 (2009)

    Article  Google Scholar 

  18. Ma, Z., Pant, G., Sheng, O.R.L.: Mining competitor relationships from online news: a network-based approach. Electron. Commence Res. Appl. 10(4), 418–427 (2011)

    Article  Google Scholar 

  19. Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)

    Book  MATH  Google Scholar 

  20. Mittermayer, M.-A.: Forecasting Intraday stock price trends with text mining techniques. In: Proceedings of the 37th Annual Hawaii International Conference on System Sciences (2004)

    Google Scholar 

  21. Paek, T., Gamon, M., Counts, S., Chickering, D.M., Dhesi, A.: Predicting the importance of newsfeed posts and social network friends. In: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, pp. 1419–1424 (2010)

    Google Scholar 

  22. Qiu, M., Yang, L., Jiang, J.: Modeling interaction features for debate side clustering. In: CIKM 2013 Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 873–878 (2013)

    Google Scholar 

  23. Schumaker, R.P., Chen, H.: Textual analysis of stock market prediction using breaking financial news: the AZFin text system. ACM Trans. Inf. Syst. 27(2), 1–19 (2009)

    Article  Google Scholar 

  24. Tetlock, P.: Giving content to investor sentiment: the role of media in the stock market. J. Finan. 62(3), 1139–1168 (2007)

    Article  Google Scholar 

  25. Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: ACL 1994 Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138 (1994)

    Google Scholar 

  26. Zhai, Z., Liu, B., Zhang, L., Xu, H., Jia, P.: Identifying evaluative sentences in online discussions. Paper presented at the 24th AAAI Conference on Artificial Intelligence (2011)

    Google Scholar 

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Acknowledgement

We are grateful to the anonymous reviewers for insightful comments. This research was supported by the Ministry of Science and Technology of Taiwan under grant MOST 103-2221-E-002-106-MY2.

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Authors and Affiliations

  1. Department of Information Management, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei City, 10617, Taiwan, ROC

    Zhong-Yong Chen & Chien Chin Chen

Authors
  1. Zhong-Yong Chen
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  2. Chien Chin Chen
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Corresponding author

Correspondence to Zhong-Yong Chen .

Editor information

Editors and Affiliations

  1. Tokyo University of Science , Noda, Japan

    Hayato Ohwada

  2. University of Tsukuba, Tokyo, Japan

    Kenichi Yoshida

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Chen, ZY., Chen, C.C. (2016). Competition Detection from Online News. In: Ohwada, H., Yoshida, K. (eds) Knowledge Management and Acquisition for Intelligent Systems . PKAW 2016. Lecture Notes in Computer Science(), vol 9806. Springer, Cham. https://doi.org/10.1007/978-3-319-42706-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-42706-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42705-8

  • Online ISBN: 978-3-319-42706-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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