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

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