Semantic Web for Effective Healthcare Systems. Группа авторов

Чтение книги онлайн.

СКАЧАТЬ retrieval. IEEE Trans. Knowl. Eng., 19, 2, 261–271, 2007.

      16. Freitas, L.A. and Vieira, R., Ontology based feature level opinion mining for Portuguese reviews. WWW’13 Companion Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, Brazil, pp. 367– 370, 2013.

      17. Ciravegna, F., Dingli, A., Petrelli, D., Wilks, Y., User-system cooperation in document annotation based on information extraction’, International Conference on Knowledge Engineering and Knowledge Management: Ontologies and the Semantic Web EKAW02. Lecture Notes in Computer Series LNCS, vol. 2473, pp. 122–137, 2002.

      18. Alani, H., Kim, S., Millard, D.E., Weal, M.J., Hall, W., Lewis, P.H., Shadbolt, N.R., Automatic ontology-based knowledge extraction from web documents. IEEE Intell. Syst., 18, 1, 14–21, 2003.

      19. Popov, B., Kiryakov, A., Ognyanoff, D., KIM – A semantic platform for information extraction. J. Nat. Lang. Eng., 10, 3–4, 375–392, 2004.

      20. Kamps, J., Marx, M., Mokken, R.J., Rijke, M.D., Using wordnet to measure semantic orientation of adjectives. Proceedings of 4th International Conference on Language Resources and Evaluation, Lisbon, Portugal, pp. 1115–1118, 2004.

21. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M., Lexicon-based methods for sentiment analysis, in: Computational linguistics, vol. 37, 2, P. Merlo (Ed.), pp. 267–307, MIT Press, Cambridge, USA, 2011.

      22. Penalver-Martinez, I., Garcia-Sanchez, F., Valencia-Garcia, R., Feature-based opinion mining through ontologies. Expert Syst. Appl., 41, 13, 5995–6008, 2014.

      23. Verma, S. and Bhattacharyya, P., Incorporating semantic knowledge for sentiment analysis’, proceedings of ICON-2008. 6th International Conference on Natural Language Processing, 2008, Macmillan Publishers, India. Available from: <https://www.cse.iitb.ac.in/~pb/papers/icon09-sa.pdf> [20-Oct-2014].

      24. Lu, Y., Duan, H., Wang, H., Zhai, C.X., Exploiting structured ontology to organize scattered online opinions. proceedings of the Twenty Third International Conference on Computational Linguistics, Beijing, pp. 734–742, 2010.

      25. Wei, W. and Gulla, J.A., Sentiment learning on product reviews via sentiment ontology tree. proceedings of the 48th Annual Meeting of the Association for Computational Linguistics ACL 10, Uppsala, Sweden, pp. 404–413, 2010.

      26. Ma, J., Xu, W., Sun, Y., Turban, E., Wang, S., Liu, O., An ontology-based text-mining method to cluster proposals for research project selection. IEEE Trans. Syst. Man Cybern. Part A Syst. Humans, 42, 3, 784–790, 2012.

      27. Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N., Ontology-based sentiment analysis of twitter posts. Expert Syst. Appl., 40 10, 4065–4074, 2013.

      28. Thakor, P. and Sasi, S., Ontology-based sentiment analysis process for social media content’. INNS Conference on Big Data 2015 Procedia Computer Science, vol. 53, pp. 199–207, 2015.

      29. Ali, F., Kwak, K.S., Kim, Y.G., Opinion mining based on fuzzy domain ontology and Support Vector Machine: A proposal to automate online review classification’. Appl. Soft Comput., 47, 3, 235–250, 2016.

      30. Popescu, O. and Etzioni, Extracting product features and opinions from reviews. Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing HLT-EMNLP, pp. 339– 346, 2005.

      31. McGlohon, M., Glance, N., Reiter, Z., Star quality: Aggregating reviews to rank products and merchants. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 114–121, 2010.

      32. Harb, A., Plantié, M., Dray, G., Roche, M., Trousset, F., Poncelet, P., Web opinion mining: How to extract opinions from blogs? Proceedings of CSTST ‘08 International Conference on Soft Computing as Transdisciplinary Science and Technology, pp. 211–217, 2008.

      33. Qiu, G., Liu, B., Bu, J., Chen, C., Expanding domain sentiment lexicon through double propagation. Comput. Ling., 37, 1, 9–27, 2008.

      34. Chawla, K., Ramteke, A., Bhattacharyya, P., IITB-sentiment-analysts: Participation in sentiment analysis in twitter semeval 2013 task. Proceedings of Seventh International Workshop on Semantic Evaluation, pp. 495–500, 2013.

35. Ding, X. and Liu, B., The utility of linguistic rules in opinion mining. Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development on Information Retrieval, SIGIR’07, Amsterdam, 2007.

      36. Poirier, D., Bothorel, C., Neef, E.C.D., Boull´e, M., Automating opinion analysis in film reviews: The case of statistic versus Linguistic approach. Language Resources and Evaluation Conference, Morocco, pp. 94–101, 2008.

      37. Na, J.C., Thet, T.T., Khoo, C., Comparing sentiment expression in movie reviews from four online genres. Online Inf. Rev., 34, 2, 317–338, 2010.

      38. Park, S., Cha, B., An, D.U., ‘Automatic multi-document summarization based on clustering and nonnegative matrix factorization. IETE Tech. Rev., 27, 2, 167–178, 2010.

      39. Basiri, M.E., Naghsh-Nilchi, A.R., Ghasem-Aghaee, N., Sentiment prediction based on dempster-shafer theory of evidence. Math. Prob. Eng., 2014, 361201, Vol. 1, pp. 1–13.

      40. Chow, C.C., A strategic service quality approach using analytic hierarchy process. Managing Service Quality, 15, 3, 278–289, 2005.

      41. Uddin, A. and Singh, V.K., A quantity–quality composite ranking of indian institutions in CS research. IETE Tech. Rev., 32, 5, 273–283, 2015.

      42. Opricovic, S. and Tzeng, G.H., Compromise solution by MCDM methods: A comparative analysis of TOPSIS and VIKOR approach. Eur. J. Oper. Res., 156, 445–455, 2004.

      43. Opricovic, S. and Tzeng, G.H., Extended VIKOR method in comparison with outranking methods’. Eur. J. Oper. Res., 178, 514–529, 2007.

      44. Amiri, M.P., Project selection for oil-fields development by using AHP and fuzzy TOPSIS methods. Expert Syst. Appl., 37, 9, 6218–6224, 2010.

      45. Velasquez, M. and Hester, P.T., An analysis of multi-criteria decision making methods. Int. J. Oper. Res., 10, 2, 56–66, 2013.

      46. Liu, D.R. and Shih, Y.Y., Integrating AHP and data mining for product recommendation based on customer lifetime value. Inf. Manage., 42, 387–400, 2005.

      47. Yazdi, A.K., Designing a mathematical model for indicators of service quality in the tourism industry based on SERVQUAL and Rembrandt methods. Int. J. Productivity Qual. Manage., 15, 4, 511–527, 2015.

      48. Buyukozkan, G. and Cifci, G., A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Syst. Appl., 39, 3, 2341–2354, 2012.

      49. Hdioud, F., Frikh, B., Ouhbi, B., Multi-criteria recommender systems based on multi-attribute decision making. Proceedings of the 15th International Conference on Information Integration and Web based Application and Services IIWAS 2013, Vienna, Austria, pp. 203–210, 2013.

50. Kang, D. and Park, Y., Review-based measurement of customer satisfaction in mobile service: Sentiment analysis and VIKOR approach. Expert Syst. Appl., 41, 4, 1041–1050, 2014.

      51. СКАЧАТЬ