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Intelligent Database Systems Lab
Presenter : JIAN-REN CHEN
Authors : Sheng-Tun Lia,b,*, Fu-Ching Tsaia
2013 , KBS
A fuzzy conceptualization model for text mining with application in opinion
polarity classification
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Intelligent Database Systems Lab
OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments
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Intelligent Database Systems Lab
MotivationMost existing document classification algorithms are easily
affected by ambiguous terms.
The ability to disambiguate for a classifier is thus as important as
the ability to classify accurately.
- opinion polarity classification
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Intelligent Database Systems Lab
ObjectivesWe propose a concept driven text classification approach based on
Formal Concept Analysis (FCA) to train a classifier using concepts
instead of documents, so as to reduce the inherent ambiguities.
We further utilize fuzzy formal concept analysis (FFCA) to take
uncertain information into consideration.
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Intelligent Database Systems Lab
Formal concept analysis
Objects: {Review6,Review7}
Attributes: {Phenomenal, Fantastic, Love}
=> formal concept
positive class:‘‘Phenomenal’’, ‘‘Fantastic’’ and ‘‘Love’’ {Review1, Review4, Review6 and Review7}
neutral class:‘‘Cover’’{Review5}
negative class:‘‘Awful’’{Review2, Review3}
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Intelligent Database Systems Lab
Formal concept analysis
positive class: {Review1, Review4, Review6, Review7}negative class:{Review2, Review3}neutral class:{Review5}
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Intelligent Database Systems Lab
Methodology - Architecture
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Intelligent Database Systems Lab
Methodologytf-idf:
Inverted ConformityFrequency (ICF):
Uniformity (Uni):tf-idf > 26 ICF < log(2)Uni > 0.2
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Intelligent Database Systems Lab
Methodology
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Intelligent Database Systems Lab
Methodology
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Experiments - Data set and evaluation
• Data set: Reuter-21578 movie review e-book review
• Evaluation
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Experiments (parameters)
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Intelligent Database Systems Lab
Experiments
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Experiments (conceptualization)
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Intelligent Database Systems Lab
Experiments
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Experiments
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Conclusions• FFCM successfully reduce the impact from textual ambiguity.
• The results from the experiments show that FFCM
outperforms other state-of-the-art algorithms for both
Reuters-21578 and two opinion polarity collections.
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Intelligent Database Systems Lab
Comments• Advantages
- the formal concepts plays an important role• Disadvantage
- α may differ from various datasets- only focuses on single-class classification
• Applications- text mining