presenter : jian-ren chen authors : sheng-tun li a,b,* , fu-ching tsai a 2013 , kbs

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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OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments

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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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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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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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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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Methodologytf-idf:

Inverted ConformityFrequency (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