Output details
11 - Computer Science and Informatics
University of Aberdeen
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Output 0 of 0 in the submission
Article title
Weakly supervised joint sentiment-topic detection from text
Type
D - Journal article
Title of journal
IEEE Transactions on Knowledge and Data Engineering
Article number
-
Volume number
24
Issue number
6
First page of article
1134
ISSN of journal
1041-4347
Year of publication
2012
URL
-
Number of additional authors
3
Additional information
<24> TKDE is one of the most prestigious journals in the field of data mining. This paper presents a novel weakly-supervised probabilistic model for sentiment analysis, which is the first topic model able to detect document-level sentiment polarity and extract sentiment-bearing topics simultaneously from text. This work builds upon a full conference paper (14.5% acceptance rate for 800+ submissions) by Lin and He, which was presented at CIKM 2009 (147 Google Scholar citations). The contribution of this work led to a research project ViolenceDET funded by EPSRC and DSTL and the proposed model has also been commercialized by Temis (www.temis.com).
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
9
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
