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Feature Extraction From Product Review Using Ontology
Feature Extraction From Product Review Using Ontology
Drashti Naik1 , Jitali Patel2
1 Dept. of CSE, Institute of Technology- Nirma University, Ahmedabad, , India .
2 Dept. of CSE, Institute of Technology- Nirma University, Ahmedabad, , India .
Correspondence should be addressed to: drashtinaik6893@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-8 , Page no. 88-93, Aug-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i8.8893

Online published on Aug 30, 2017

Copyright © Drashti Naik, Jitali Patel . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 
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IEEE Style Citation: Drashti Naik, Jitali Patel, “Feature Extraction From Product Review Using Ontology”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.8, pp.88-93, 2017.

MLA Style Citation: Drashti Naik, Jitali Patel "Feature Extraction From Product Review Using Ontology." International Journal of Computer Sciences and Engineering 5.8 (2017): 88-93.

APA Style Citation: Drashti Naik, Jitali Patel, (2017). Feature Extraction From Product Review Using Ontology. International Journal of Computer Sciences and Engineering, 5(8), 88-93.
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Abstract :
Opinion mining is accepting more attention because of the development of blogs, e-commerce, news, reports, forums and additional web sources where individuals tend to express their opinions. Different people have different opinions. People`s thought may vary according to the domain and opinion may contain both positive and negative words. For a product, user may like or dislike some of its features. Filtering this review and extract domain related features is the important task of this paper. In this paper, ontology is used to extract the features and adjectives are used as the sentiment word. Sentiment Analysis is used to obtain positive or negative feature of the review.
Key-Words / Index Term :
Ontology,Natural-language-Processing,SentimentAnalysis
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