A Survey : Ontology Based Information Retrieval For Sentiment Analysis

Authors

  • Gopi A. Patel   Computer Engineering Department, Silver Oak College of Engineering and Technology, Ahmedabad, Gujarat, India
  • Nidhi Madia  Computer Engineering Department, Silver Oak College of Engineering and Technology, Ahmedabad, Gujarat, India

Keywords:

Ontology, Sentiment Analysis, Semantic Web, Web Mining

Abstract

The rapidly growing data on the web has created a big challenge for directing the user to the web pages in their areas of interest. Sentiment analysis or Opinion mining plays an important role in finding the area of interest based on user’s previous actions. Social networking portals have been widely used for expressing opinions in the public domain. Text based sentiment classifiers often prove inefficient. Semantic web is the solution for Searching relevant information from huge repository of unstructured web data. Semantic web leads the idea of ontology as background knowledge represents the concepts and the relationship in specialized domain. The basic idea behind this survey is to take domain ontology for providing more elaborate sentiment scores. We discuss an approach where information retrieved from web and ontology is created before sentiment classification and focuses on how to classify the semantic orientation of text.

References

  1. B. Pang and L. Lee. “Opinion mining and sentiment analysis”. Foundations and Trends in Information Retrieval, 2(1-2):1–135, 1 2008.
  2. Tim Berners-Lee, James Hendler and Ora Lassila .The Semantic Web Scientific American: Feature Article: The Semantic Web: May 2001.
  3. GerdStumme, Andreas Hotho, Bettina Berendt, “Semantic Web Mining State of the Art and Future Directions”, ESELIVER, Knowledge and Data Engineering Group, University of Kassel, D-34121 Kassel.
  4. C.S.Bhatia, Dr. Suresh Jain,“Semantic Web Mining: Using Ontology Learning and Grammatical Rule Inference Technique”, IEEE, Department of computer engineering, Mewar University, Chittorgarh- 2011
  5. T. R. Gruber.“Towards Principles for the Design of      Ontologies used for Knowledge Sharing”. In N. Guarino and R. Poli,editors, Formal Ontology in Conceptual Analysis and Knowledge Representation, Deventer, Netherlands, 1993. Kluwer.
  6. Hakan Yilmaz, “Using Ontology Based Web Usage Mining and Object Clustering For Recommendation”, the Graduate School Of Natural And Applied Sciences Of Middle East Technical University, May-2010.
  7. Abd-Elrahman Elsayed1, Samhaa R. El-Beltagy2, Mahmoud Rafea1, Osman Hegazy3,“Applying data mining for ontology building”, 1 The Central Laboratory for Agricultural Expert Systems, Giza, Egypt.2 Faculty of Computers and Information, Computer Science Department, Cairo University Giza, Egypt. 3Faculty of Computers and Information, Information System Department, Cairo University Giza, Egypt.
  8. Katarzyna Wójcik, Janusz Tuchowski, “Ontology Based Approach to Sentiment analysis”.June-2014.
  9. Sam, K. M. I. Chatwin, C. (2013, Grudzień). “Ontology-Based Sentiment Analysis Model of Customer”, International Journal of e-Education, e-Business, e-Management and e-Learning, 3(6), strony 477-482.
  10. Cooley, R. and Mobasher, B. and Srivastava, J. (1997) Web mining: “Information and pattern discovery on the World Wide Web”. In Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'97), Los Alamitos.
  11. Kontopoulos, E., Berberidis, C., Dergiades, T., Bassiliades, N., “Ontology-based Sentiment Analysis of Twitter Posts”, Expert Systems with Applications (2013)
  12. Cheng Mingzhi, Xin Yang, Bao Jingbing, Wang Cong and Yang Yixian. “A Random Walk Method for Sentiment Classification”, Second International Conference on Future Information Technology and Management Engineering, 2009.
  13. P. D. Turney. “Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews”. Proc of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), pp. 417-424.
  14. Pang, L. Lee, and S. Vaithyanathan. “Thumbs up? Sentiment classification using machine learning techniques”. Proc of EMNLP-02, the Conference on Empirical Methods in Natural Language processing.
  15. BalakrishnanGokulakrishnan , Pavalanathan Priyanthan , ThiruchittampalamRagavan ,Nadarajah Prasath and  AShehan Perera,”Opinion mining and sentiment analysis on a twitter data stream”, The International Conference on Advances in ICT for Emerging Regions - ICTer 2012 : 182-188.
  16. Zhen Niu, Zelong Yin and Xiangyu Kong, “Sentiment Classification for Microblog by Machine Learning”, 2012 Fourth International Conference on Computational and Information Sciences
  17. Malhar Anjaria and Ram Mahana Reddy Guddeti, “Influence Factor Based Opinion Mining of Twitter Data Using Supervised Learning”, National Institute of Technology Karnataka, Surathkal, Mangalore - 575025, India.
  18. Hai-Bing Ma, Yi-Bing Geng and Jun-Rui Qiu, “Analysis Of Three Methods For Web-Based Opinion Mining”, proceedings of the 2011 international conference on machine learning and cybernetics, guilin, 10-13 july, 2011.
  19. Polpinij, J. & Ghose, A. K. (2008). “An Ontology-Based sentiment Classification Methodology For Online Consumer Reviews”, IEEE/WIC/ACM International Conference on Intelligent Agent Technology (pp. 518-524).

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2017-12-31

Issue

Volume 2, Issue 2, March-April-2016

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Gopi A. Patel , Nidhi Madia, " A Survey : Ontology Based Information Retrieval For Sentiment Analysis, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.460-465, March-April-2016.