A Novel Multi Context Prediction and Mining Model on Online Opinions Evolutions against Food Hazard

Authors(2) :-N. Hamsaleka, Dr. V. Kathiresan

Proliferation of location based social Network yields the variety of opinion related to food quality and hazards as data. Many data analysis technique has been proposed to determine specific fact. Despite of various factors, Analysis of collective inference of food related review on multi context stands primary important of the current research. Though we propose a novel technique entitled as Multi Context Prediction and Mining Model on online opinions Evolutions. It exploits the correlated multi context based on the conceptual similarity at different time period in terms of time series analysis and feature representation. Further it reduces the invariance of feature evolution and concept evolution to more extent. This model involves complex relationship determination among the instances. Variation of related instance evolved over time can be easily extracted. It works as multi context evolution inference model. Additionally opinion adaptation model is been defined to categorize the Target opinion on particular event using probability distribution model or markov random field. Also it helps in reducing the labelling efforts of the opinion by leveraging labelled data from various criterias.The extensive experimental results prove that proposed model outperforms the state of art approaches in terms of precision, Recall and F Measure.

Authors and Affiliations

N. Hamsaleka
Research Scholar, Department of Computer Science, Bharathiar University, Coimbatore, Tamil Nadu, India
Dr. V. Kathiresan
Head of the Department, Department of Computer Applications (PG), Dr. SNS Rajalakshmi college of Arts and Science, Coimbatore, Tamil Nadu, India

Food Hazard Prediction, Opinion Evolution, Opinion Inference, Online Review, Opinion Mining

  1. Y. Liu, X. Huang, A. An, and X. Yu, “Modeling and predicting the helpfulness of online reviews,” in Proc. 8th Int. Conf. Data Mining, 2008, pp. 443–452.
  2. P. Tsaparas, A. Ntoulas, and E. Terzi, “Selecting a comprehensive set of reviews,” in Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2011, pp. 168–176.
  3. Bo Liu,Yanshan Xiao, Philip S. Yu, Zhifeng Hao, Longbing Cao "An Efficient Approach for Outlier Detection with Imperfect Data Labels" IEEE Transactions on Knowledge and Data Engineering in Volume: 26, Issue: 7, July 2014
  4. R. Jin and G. Agrawal. Efficient decision tree construction on streaming data. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 571–576, 2003.
  5. M. Masud, J. Gao, L. Khan, J. Han, and B. Thuraisingham. Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans. Knowledge and Data Engineering, 23(6):859–874, 2011.
  6. K. Zhang, V. W. Zheng, Q. Wang, J. T. Kwok, Q. Yang, and I. Marsic, “Covariate shift in hilbert space: A solution via surrogate kernels,” in Int. Conf. Mach. Learn., 2013.
  7. Dingqi Yang, Daqing Zhang, and Bingqing Qu. Participatory cultural mapping based on collective behavior data in locationbased social networks. ACM Trans. Intell. Syst. Technol., 7(3):30:1– 30:23, 2016.
  8. J. Tang, S. Wu, J. Sun, and H. Su, “Cross-domain collaboration recommendation,” in ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2012.
  9. W. Yu, R. Zhang, X. He, and C. Sha, “Selecting a diversified set of reviews,” in Proc. 15th Asia-Pacific Web Conf., 2013, pp. 721–733.
  10. L. Yu, P. Cui, F. Wang, C. Song, and S. Yang. From Micro to Macro: Uncovering and predicting information cascading process with behavioral dynamics. In ICDM, 2015.

Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 721-725
Manuscript Number : CSEIT1835179
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

N. Hamsaleka, Dr. V. Kathiresan , "A Novel Multi Context Prediction and Mining Model on Online Opinions Evolutions against Food Hazard", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.721-725, May-June-2018.
Journal URL : http://ijsrcseit.com/CSEIT1835179

Follow Us

Contact Us