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

Teja Santosh Dandibhotla, Dr. Vishnu Vardhan Bulusu

 

Abstract: 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.
Keywords: Opinion mining, Feature, Sentiment, Resource Description Framework, Ontology

Full PDF Download

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.