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Prominent Feature Extraction for Sentiment AnalysisSentiment Analysis Using ConceptNet Ontology and Context Information

Prominent Feature Extraction for Sentiment Analysis: Sentiment Analysis Using ConceptNet Ontology... [Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this chapter, we propose a novel sentiment analysis model based on commonsense knowledge extracted from ConceptNet-based ontology and context information. ConceptNet-based ontology is used to determine the domain-specific concepts which in turn produced the domain-specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain-specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Prominent Feature Extraction for Sentiment AnalysisSentiment Analysis Using ConceptNet Ontology and Context Information

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-25341-1
Pages
63 –75
DOI
10.1007/978-3-319-25343-5_5
Publisher site
See Chapter on Publisher Site

Abstract

[Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this chapter, we propose a novel sentiment analysis model based on commonsense knowledge extracted from ConceptNet-based ontology and context information. ConceptNet-based ontology is used to determine the domain-specific concepts which in turn produced the domain-specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain-specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.]

Published: Dec 15, 2015

Keywords: Sentiment Analysis Model; ConceptNet; Unstructured Natural Language Text; Opinion Words; Sentiment Lexicon

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