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Simple Search
Hits 1 – 13 of 13
1
Construction of a sentimental word dictionary
Eduard C. Dragut
;
Clement Yu Prasad
;
Weiyi Meng
In: http://www.cs.binghamton.edu/~meng/pub.d/cikm785-dragut.pdf (2010)
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2
Recognition and classification of noun phrases in queries for effective retrieval
Wei Zhang
;
Shuang Liu
;
Clement Yu
...
In: http://www.cs.binghamton.edu/~meng/pub.d/p711-zhang-cikm07a.pdf (2005)
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3
Word sense disambiguation in queries
Shuang Liu
;
Clement Yu
In: http://www.cs.binghamton.edu/~meng/pub.d/cikm05Shuang.pdf (2005)
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4
Recognition and classification of noun phrases in queries for effective retrieval
Wei Zhang
;
Shuang Liu
;
Clement Yu
...
In: http://www.cs.uic.edu/~wzhang/cikm2007/zhang_cikm_2007_recognition.pdf (2005)
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5
An effective approach to document retrieval via utilizing wordnet and recognizing phrases
Shuang Liu
;
Fang Liu
;
Clement Yu
...
In: http://www.cs.binghamton.edu/~meng/pub.d/p266-liu.pdf (2004)
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6
An effective approach to document retrieval via utilizing wordnet and recognizing phrases
Shuang Liu
;
Fang Liu
;
Clement Yu
...
In: http://www.cs.uic.edu/~sliu/p096-liu.pdf (2004)
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7
Deriving Customized Integrated Web Query Interfaces
Eduard C. Dragut
;
Fang Fang
;
Clement Yu
...
In: http://www.cs.binghamton.edu/~meng/pub.d/WI09.pdf
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8
Faceted Models of Blog Feeds
Lifeng Jia
;
Clement Yu
;
Weiyi Meng
In: http://www.cs.binghamton.edu/~meng/pub.d/p2279-jia-cikm13.pdf
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9
Abstract UIC at TREC-2004: Robust Track
Shuang Liu
;
Chaojing Sun
;
Clement Yu
In: http://trec.nist.gov/pubs/trec13/papers/uil-chicago.liu.robust.pdf
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10
UIC at TREC 2010 Faceted Blog Distillation
Lifeng Jia
;
Clement Yu
In: http://trec.nist.gov/pubs/trec19/papers/univ.illinois-chicago.blog.rev2.pdf
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11
Abstract UIC at TREC-2003: Robust Track (Draft)
Shuang Liu
;
Clement Yu
In: http://trec.nist.gov/pubs/trec12/papers/uillinois-chicago.robust.pdf
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12
Topic Sentiment Change Analysis
Yu Jiang
;
Clement Yu
In: http://www.cs.binghamton.edu/~meng/pub.d/MLDM-camera_ready.pdf
Abstract:
Abstract. Public opinions on a topic may change over time. Topic Sentiment change analysis is a new research problem consisting of two main components: (a) mining opinions on a certain topic, and (b) detect significant changes of sentiment of the opinions on the topic and identify possible reasons causing each such change. In this paper, we discuss topic sentiment change analysis using data on the Web. We adopt probabilistic topic model and language grammar based sentiment analysis techniques, and integrate them together into a topic level sentiment analysis method. This method is capable of analyzing sentiment and identifying sentiment changes of a given topic from a set of documents covering this topic and possibly other topics. In addition, as the contents of relevant topics are differentiated, our method is also able to identify hot events which are possible causes of a sentiment change. Experimental results show that our method is very promising.
Keyword:
opinion mining
;
sentiment change
;
topic model
URL:
http://www.cs.binghamton.edu/~meng/pub.d/MLDM-camera_ready.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.299.4861
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13
Polarity Consistency Checking for Domain Independent Sentiment Dictionaries
Eduard C. Dragut
;
Hong Wang
;
Prasad Sistla
...
In: http://www.cs.binghamton.edu/%7Emeng/pub.d/tkde-2015.pdf
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