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A fuzzy conceptualization model
for text mining with application in opinion
polarity classification
Presenter : JIAN-REN CHEN
Authors
: Sheng-Tun Lia,b,*, Fu-Ching Tsaia
2013 , KBS
Intelligent Database Systems Lab
Outlines
 Motivation
 Objectives
 Methodology
 Experiments
 Conclusions
 Comments
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Motivation
Most 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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Objectives
We 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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Formal concept analysis
Objects: {Review6,Review7}
positive class:
Attributes: {Phenomenal,
Love}
‘‘Phenomenal’’,
‘‘Fantastic’’Fantastic,
and ‘‘Love’’
{Review1,
=> formalReview4,
conceptReview6 and Review7}
negative class:
‘‘Awful’’
{Review2, Review3}
neutral class:
‘‘Cover’’
{Review5}
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Formal concept analysis
positive class:
{Review1, Review4, Review6, Review7}
negative class:
{Review2, Review3}
neutral class:
{Review5}
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Methodology - Architecture
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Methodology
tf-idf:
Inverted Conformity
Frequency (ICF):
Uniformity (Uni):
tf-idf > 26
ICF < log(2)
Uni > 0.2
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Methodology
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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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Experiments
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Experiments (conceptualization)
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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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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
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