Analyzing online review helpfulness using a Regressional ReliefF-enhanced text mining method

ACM Transactions on Management Information Systems

Volumn 3, Issue 2, 2012, Pages

  Ngo Ye, Thomas L ^a   Sinha, Atish P ^b  

^a DALTON STATE COLLEGE   (United States)

^b UNIVERSITY OF WISCONSIN MILWAUKEE   (United States)

Author keywords

Business intelligence; Dimension reduction; Online reviews; Regressional ReliefF; Text mining

Indexed keywords

CUSTOMER FEEDBACK; CUSTOMER REVIEW; DIMENSION REDUCTION; DIMENSION REDUCTION METHOD; DIMENSION REDUCTION TECHNIQUES; E-MARKETPLACES; FEATURE GROUPING; FEATURE SELECTION METHODS; INFORMATION OVERLOADS; ONLINE CUSTOMERS; RELIEFF; SELECTION TECHNIQUES; SOCIAL MEDIA; TEXT CLASSIFICATION; TEXT MINING; WEB 2.0;

CLASSIFICATION (OF INFORMATION); COMPETITIVE INTELLIGENCE; DATA MINING; INFORMATION DISSEMINATION; INTELLIGENT AGENTS;

TEXT PROCESSING;

EID: 84872115050     PISSN: 2158656X     EISSN: 21586578     Source Type: Journal    
DOI: 10.1145/2229156.2229158     Document Type: Review

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