Prominent feature extraction for review analysis: An empirical study

Journal of Experimental and Theoretical Artificial Intelligence

Volumn 28, Issue 3, 2016, Pages 485-498

  Agarwal, Basant ^a   Mittal, Namita ^a  

^a MALAVIYA NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

composite features; feature selection methods; information gain; machine learning; minimum redundancy maximum relevancy (mRMR); sentiment analysis

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; FEATURE EXTRACTION; IMAGE RETRIEVAL; LEARNING ALGORITHMS; LEARNING SYSTEMS; REDUNDANCY; REVIEWS;

COMPOSITE FEATURES; FEATURE SELECTION METHODS; INFORMATION GAIN; MINIMUM REDUNDANCY MAXIMUM RELEVANCY (MRMR); SENTIMENT ANALYSIS;

CLASSIFICATION (OF INFORMATION);

EID: 84913568992     PISSN: 0952813X     EISSN: 13623079     Source Type: Journal    
DOI: 10.1080/0952813X.2014.977830     Document Type: Article

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