1. Blitzer, J., McDonald, R., Pereira, F.: Domain adaptation with structural correspondence learning. In: Proc. Conf. on EMNLP (2006)

   Google Scholar 

2. Cano, E., He, Y., Liu, K., Zhao, J.: A weakly supervised bayesian model for violence detection in social media. In: Proc. 6th IJCNLP 2013 (2013)

   Google Scholar 

3. Cano, A.E., Varga, A., Rowe, M., Ciravegna, F., He, Y.: Harnessing linked knowledge source for topic classification in social media. In: Proc. 24th ACM Conf. on Hypertext and Social Media, Paris, France (2013)

   Google Scholar 

4. Caruana, R.: Multitask learning.  28(1), 41–75 (1997)

   Google Scholar 

5. Chen, G.H., Nikolov, S., Shah, D.: A latent source model for nonparametric time series classification. In: Advances in Neural Information Processing Systems (2013)

   Google Scholar 

6. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods. Cambridge University Press (2000)

   Google Scholar 

7. Daumé, I.: Frustratingly easy domain adaptation. In: Proceedings of the 2007 ACL (2007)

   Google Scholar 

8. Diao, Q., Jiang, J., Zhu, F., Lim, E.-P.: Finding bursty topics from microblogs. In: Proc. 50th Annual Meeting of the ACL, Jeju Island, Korea (2012)

   Google Scholar 

9. Dries, A., Rückert, U.: Adaptive concept drift detection. Stat. Anal. Data Min. 2(56) (2009)

   Google Scholar 

10. Genc, Y., Sakamoto, Y., Nickerson, J.V.: Discovering context: Classifying tweets through a semantic transform based on wikipedia. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) FAC 2011. LNCS, vol. 6780, pp. 484–492. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

11. He, Y.: Incorporating sentiment prior knowledge for weakly supervised sentiment analysis. ACM Transactions on Asian Language Information Processing 11(2), 4:1–4:19 (2012)

    Google Scholar 

12. He, Y., Lin, C., Gao, W., Wong, K.-F.: Tracking sentiment and topic dynamics from social media. In: Proc. of the Sixth Int. Conf. on Weblogs and Social Media, Dublin, Ireland (2012)

    Google Scholar 

13. Jones, K.S., Walker, S., Robertson, S.E.: A probabilistic model of information retrieval: Development and comparative experiments. Inf. Process. Manage. 36(6), 779–808 (2000)

    Article  Google Scholar 

14. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proc. 14th IJCAI, vol. 2 (1995)

    Google Scholar 

15. Lv, W., Xu, W., Guo, J.: Transfer learning in classification based on semantic analysis. In: 2nd Int. Conf. on ICCSNT (2012)

    Google Scholar 

16. Milikic, N., Jovanovic, J., Stankovic, M.: Discovering the dynamics of terms semantic relatedness through twitter. In: Proceedings, 1st Workshop on #MSM 2011 (2011)

    Google Scholar 

17. Muñoz García, O., García-Silva, A., Corcho, O., de la Higuera Hernández, M., Navarro, C.: Identifying Topics in Social Media Posts using DBpedia. In: Proc. of the NEM Summit (2011)

    Google Scholar 

18. Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. on Knowl. and Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

19. Phan, X.-H., Nguyen, L.-M., Horiguchi, S.: Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: Proc. 17th Int. Conf. on World Wide Web, WWW 2008, Beijing, China (2008)

    Google Scholar 

20. Porter, M.: An algorithm for suffix stripping. Program 14(3) (1980)

    Google Scholar 

21. Saif, H., Fernandez, M., He, Y., Alani, H.: Senticircles for contextual and conceptual semantic sentiment analysis of twitter. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 83–98. Springer, Heidelberg (2014)

    Google Scholar 

22. Salzberg, S.L., Fayyad, U.: On comparing classifiers: Pitfalls to avoid and a recommended approach. In: Data Mining and Knowledge Discovery, pp. 317–328 (1997)

    Google Scholar 

23. Song, Y., Wang, H., Wang, Z., Li, H., Chen, W.: Short text conceptualization using a probabilistic knowledgebase. In: Int. Joint. Conf. of AI (IJCAI). IJCAI/AAAI (2011)

    Google Scholar 

24. Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in twitter to improve information filtering. In: Proc. of the Int. ACM SIGIR (2010)

    Google Scholar 

25. Thrun, S.: Is learning the n-th thing any easier than learning the first? In: Advances in Neural Information Processing Systems (1996)

    Google Scholar 

26. Varga, A., Cano, A., Rowe, M., Ciravegna, F., He, Y.: Linked knowledge sources for topic classification of microposts: A semantic graph-based approach. In: JWS: Science, Services and Agents on the WWW (2014)

    Google Scholar 

27. Zhao, X., Shu, B., Jiang, J., Song, Y., Yan, H., Li, X.: Identifying event-related bursts via social media activities. In: Proc. of the Joint Conference on EMNLP, Jeju Island, Korea (2012)

    Google Scholar 

Download references