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Prominent Feature Extraction for Sentiment AnalysisSemantic Parsing Using Dependency Rules

Prominent Feature Extraction for Sentiment Analysis: Semantic Parsing Using Dependency Rules [Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In the frame of biologically inspired machine learning approaches, finding good feature sets is particularly challenging yet very important. In this chapter, we focus on this fundamental issue of the sentiment analysis task. Specifically, we employ concepts as features and present a concept extraction algorithm to extract semantic features that exploit semantic relationships between words in natural language text. Additional conceptual information of a concept is obtained using the ConceptNet ontology. Concepts extracted from text are sent as queries to ConceptNet to extract their semantics. Further, we select important concepts and eliminate redundant concepts using the Minimum Redundancy and Maximum Relevance feature selection technique. All selected concepts are then used to build a machine learning model that classifies a given document as positive or negative] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Prominent Feature Extraction for Sentiment AnalysisSemantic Parsing Using Dependency Rules

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Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-25341-1
Pages
47 –61
DOI
10.1007/978-3-319-25343-5_4
Publisher site
See Chapter on Publisher Site

Abstract

[Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In the frame of biologically inspired machine learning approaches, finding good feature sets is particularly challenging yet very important. In this chapter, we focus on this fundamental issue of the sentiment analysis task. Specifically, we employ concepts as features and present a concept extraction algorithm to extract semantic features that exploit semantic relationships between words in natural language text. Additional conceptual information of a concept is obtained using the ConceptNet ontology. Concepts extracted from text are sent as queries to ConceptNet to extract their semantics. Further, we select important concepts and eliminate redundant concepts using the Minimum Redundancy and Maximum Relevance feature selection technique. All selected concepts are then used to build a machine learning model that classifies a given document as positive or negative]

Published: Dec 15, 2015

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