Weakly supervised joint sentiment-topic detection from text

IEEE Transactions on Knowledge and Data Engineering

Volumn 24, Issue 6, 2012, Pages 1134-1145

  Lin, Chenghua ^a,b   He, Yulan ^b   Everson, Richard ^a   Rüger, Stefan ^b  

^a UNIVERSITY OF EXETER   (United Kingdom)

^b OPEN UNIVERSITY   (United Kingdom)

Author keywords

joint sentiment topic (JST) model; latent Dirichlet allocation (LDA); opinion mining; Sentiment analysis

Indexed keywords

AUTOMATED TOOLS; DATA SETS; DIFFERENT DOMAINS; HIGHLY-PORTABLE; LABELED DOCUMENTS; LATENT DIRICHLET ALLOCATIONS; MODEL-BASED OPC; MODELING PROCESS; OPINION MINING; PROBABILISTIC MODELING; SEMI-SUPERVISED; SENTIMENT ANALYSIS; SENTIMENT CLASSIFICATION; SUBJECTIVE INFORMATION;

STATISTICS;

DATA MINING;

EID: 84860495542     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2011.48     Document Type: Article

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