• Vol 6, No 2 (2015)
  • Electrical, Electronics, and Computer Engineering

Obtaining Feature- and Sentiment-Based Linked Instance RDF Data from Unstructured Reviews using Ontology-Based Machine Learning

D. Teja Santosh, B. Vishnu Vardhan


Cite this article as:

Santosh, D.T., Vardhan, B.V., 2015. Obtaining Feature- and Sentiment-Based Linked Instance RDF Data from Unstructured Reviews using Ontology-Based Machine Learning. International Journal of Technology. Volume 6(2), pp. 198-206

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D. Teja Santosh Department of Computer Science & Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India
B. Vishnu Vardhan Jawaharlal Nehru Technological University College of Engineering, Kodimyal mandal, Karimnagar Dist. Telangana, India
Email to Corresponding Author

Abstract
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Online reviews have a profound impact on the customer or newbie who want to purchase or consume the product via web 2.0 e-commerce. Online reviews contain features which form half of the analysis in opinion mining. Most of the today’s systems work on the summarization taking the average of the obtained features and their sentiments leading to structured review information. Often the context surrounding the feature is undermined which helps in clearly classifying the sentiment of the review. Web 3.0 based machine interpretable Resource Description Framework (RDF) also structures these unstructured reviews in the form of features and sentiments obtained from traditional preprocessing and extraction techniques with the context data also provided for future ontology based analysis taking support of Wordnet 2.1 lexical database for word sense disambiguation and Sentiwordnet 3.0 scores used for sentiment word extraction. Many popular RDF vocabularies are helpful in the creation of such machine process-able data. In the work to follow, this instance RDF forms the basis for creating/upgrading the (available) OWL Ontology that can be used as structured data model with rich semantics towards supervised machine learning generating sentiment categories and are validated for precise sentiments. These are sent back to the interface as corresponding {feature, sentiment} pair so that reviews are filtered clearly and helps in satisfying the feature set of the customer.

Opinion mining, Feature, Sentiment, Resource Description Framework, Ontology

References

Alekh Agarwal and Pushpak Bhattacharyya, Sentiment Analysis: A New Approach for Effective Use of Linguistic Knowledge and Exploiting Similarities in a Set of Documents to be Classified, Proceedings of ICON, 2005.

Bo Pang and Lillian Lee, Opinion mining and sentiment analysis, Foundations and Trends in Information Retrieval Vol. 2, No 1-2 (2008) 1–135.

Selver Softic and Michael Hausenblas, Towards Opinion Mining Through Tracing Discussions on the Web, 2008.

Paul Buitelaar et al., Linguistic Linked Data for Sentiment Analysis, August 2013.

Minqing Hu, Bing Liu, Mining and summarizing customer reviews, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA.

Verma, S., & Bhattacharyya, P., Incorporating semantic knowledge for sentiment analysis, Proceedings of ICON, 2009.

Christopher C. Yang , Y. C. Wong , Chih-Ping Wei, Classifying web review opinions for consumer product analysis, Proceedings of the 11th International Conference on Electronic Commerce, August 12-15, 2009, Taipei, Taiwan.

Polpinij, J., & Ghose, A. K., An ontology-based sentiment classification methodology for online consumer reviews, Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01 (pp. 518-524). IEEE Computer Society, December 2008.

Peñalver-Martínez, Isidro, Rafael Valencia-García, and Francisco García-Sánchez, Ontology-guided approach to feature-based opinion mining, In Natural Language Processing and Information Systems, pp. 193-200. Springer Berlin Heidelberg, 2011.

Freitas, Larissa A., and Renata Vieira, Ontology based feature level opinion mining for portuguese reviews, In Proceedings of the 22nd international conference on World Wide Web companion, pp. 367-370. International World Wide Web Conferences Steering Committee, 2013.

Christiane Fellbaum (1998), WordNet: An Electronic Lexical Database. Bradford Books.

Baccianella, Stefano, Andrea Esuli, and Fabrizio Sebastiani, SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining, In LREC, vol. 10, pp. 2200-2204. 2010.

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