Obtaining Feature-and Sentiment-based Linked Instance RDF Data from Unstructured Reviews using Ontology-based Machine Learning
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 o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universitas Indonesia
2015-04-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | http://ijtech.eng.ui.ac.id/article/view/1343 |
| _version_ | 1797967287151493120 |
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| author | D. Teja Santosh B. Vishnu Vardhan |
| author_facet | D. Teja Santosh B. Vishnu Vardhan |
| author_sort | D. Teja Santosh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T02:28:51Z |
| format | Article |
| id | doaj.art-28efffdeafdb4d11980ac0d19a3909f2 |
| institution | Directory Open Access Journal |
| issn | 2086-9614 2087-2100 |
| language | English |
| last_indexed | 2024-04-11T02:28:51Z |
| publishDate | 2015-04-01 |
| publisher | Universitas Indonesia |
| record_format | Article |
| series | International Journal of Technology |
| spelling | doaj.art-28efffdeafdb4d11980ac0d19a3909f22023-01-02T21:49:00ZengUniversitas IndonesiaInternational Journal of Technology2086-96142087-21002015-04-016219820610.14716/ijtech.v6i2.13431343Obtaining Feature-and Sentiment-based Linked Instance RDF Data from Unstructured Reviews using Ontology-based Machine LearningD. Teja Santosh0B. Vishnu Vardhan1Department of Computer Science & Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, IndiaJawaharlal Nehru Technological University College of Engineering, Kodimyal mandal, Karimnagar Dist. Telangana, IndiaOnline 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.http://ijtech.eng.ui.ac.id/article/view/1343Opinion mining, Feature, Sentiment, Resource Description Framework, Ontology |
| spellingShingle | D. Teja Santosh B. Vishnu Vardhan Obtaining Feature-and Sentiment-based Linked Instance RDF Data from Unstructured Reviews using Ontology-based Machine Learning International Journal of Technology Opinion mining, Feature, Sentiment, Resource Description Framework, Ontology |
| title | Obtaining Feature-and Sentiment-based Linked Instance RDF Data from Unstructured Reviews using Ontology-based Machine Learning |
| title_full | Obtaining Feature-and Sentiment-based Linked Instance RDF Data from Unstructured Reviews using Ontology-based Machine Learning |
| title_fullStr | Obtaining Feature-and Sentiment-based Linked Instance RDF Data from Unstructured Reviews using Ontology-based Machine Learning |
| title_full_unstemmed | Obtaining Feature-and Sentiment-based Linked Instance RDF Data from Unstructured Reviews using Ontology-based Machine Learning |
| title_short | Obtaining Feature-and Sentiment-based Linked Instance RDF Data from Unstructured Reviews using Ontology-based Machine Learning |
| title_sort | obtaining feature and sentiment based linked instance rdf data from unstructured reviews using ontology based machine learning |
| topic | Opinion mining, Feature, Sentiment, Resource Description Framework, Ontology |
| url | http://ijtech.eng.ui.ac.id/article/view/1343 |
| work_keys_str_mv | AT dtejasantosh obtainingfeatureandsentimentbasedlinkedinstancerdfdatafromunstructuredreviewsusingontologybasedmachinelearning AT bvishnuvardhan obtainingfeatureandsentimentbasedlinkedinstancerdfdatafromunstructuredreviewsusingontologybasedmachinelearning |