Journal Title : International Journal of Modern Trends in Engineering and Science
Paper Title : RULE BASED METHOD AND CROSS DOMAIN FEATURES FOR SENTIMENT SENSITIVE ANALYSIS
Volume 03 Issue 12 2016
ISSN no: 2348-3121
Page no: 37-41
Abstract – The new development tools ,frame work are appeared ,they are called as CMS(Content Management system),this framework are used to allows the nimble website development, the system are used for an easy installation ,content publication and edition , enabling a common user to publish online content are being a computer expert or a programmer. Because of facilities more forums, blogs and specialized web sites have being developed, increased dramatically the number of content generated by ordinary users. This content is unstructured data in the form of the free text, the recovery and extraction of meaningful information depends on the specialized techniques. This work is used to explore the use of a techniques focused on the analysis of user to be generated content in the e-commerce context.
Keywords— Rule Based Method, Cross Domain, sentiment classification, learning Algorithm
- B. Pang and L. Lee, “Opinion mining and sentiment analysis,” Found. Trends Inf. Retrieval, vol. 2, nos. 1/2, pp. 1–135, 2008.
- Y. Lu, C. Zhai, and N. Sundaresan, “Rated aspect summarization of short comments,” in Proc. 18th Int. Conf. World Wide Web, 2009, pp. 131–140.
- T.-K. Fan and C.-H. Chang, “Sentiment-oriented contextual Advertising,” Knowl. Inf. Syst., vol. 23, no. 3, pp. 321–344, 2010.
- M. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proc. 10th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2004, pp. 168–177.
- C. D. Manning and H. Schutze, Foundations of Statistical Natural Language Processing. Cambridge, MA, USA: MIT Press, 2002.
- H. Daum_e III, A. Kumar, and A. Saha, “Co-regularization based semi-supervised domain adaptation,” in Proc. Adv. Neural Inf. Process. Syst. 23, 2010. pp. 478–486.
- D. Lopez-Paz, J. M. Hernandez-Lobato, and B. Scholkopf, “Semisupervised domain adaptation with non-parametric copulas,” inProc. Adv. Neural Inf. Process. Syst. 25, 2012, pp. 674–682.
- H. Daum_e III, “Frustratingly easy domain adaptation,” in Proc. 45th Annu. Meeting Assoc. Comput. Linguistics, 2007, pp. 256–263.
- J. Blitzer, R. McDonald, and F. Pereira, “Domain adaptation with Structural correspondence learning,” in Proc. Conf. Methods Natural Language Process. 2006, pp. 120–128.
- J. Blitzer, M. Dredze, and F. Pereira, “Biographies, bollywood, Boom-boxes and blenders: Domain adaptation for sentiment classification,” in Proc. 45th Annu. Meeting Assoc. Comput. Linguistics, 2007, pp. 440–447.
- S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen, “Cross-domain Sentiment classification via spectral feature alignment,” in Proc. 19th Int. Conf. World Wide Web, 2010, pp. 751–760.
- U. Luxburg, “A tutorial on spectral clustering,” Statist. Comput. vol. 17, no. 4, pp. 395–416, 2007.
- T. Mu, J. Y. Goulermas, J. Tsujii, and S. Ananiadou, “Proximity based frameworks for generating embeddings from multi-output data,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11,pp. 2216–2232, Nov. 2012.
- H. Golub and C. F. V. Loan, Matrix Computations. Baltimore, MD, USA: John Hopkins Univ. Press, 1996.
- X.-T. Yuan and T. Zhang, “Truncated power method for sparse eigenvalue problems,” J. Mach. Learn. Res., vol. 14, pp. 899–925, 2013.
- N. Halko, P. G. Martinsson, and J. A. Tropp, “Finding structure with randomness: Probabilistic algorithms for constructing Approximate matrix decompositions,” SIAM Rev, vol. 53, no. 2, pp. 217–288, 2010.
- B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment Classification using machine learning techniques,” in Proc. Conf Empirical Methods Natural Language Process. 2002, pp. 79–86.
- S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan, “A theory of learning from different domains,” Mach. Learn., vol. 79, pp. 151–175, 2009.
- S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345–1359, Oct. 2010.