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


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


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


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.


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}


Formal concept analysis

positive class: {Review1, Review4, Review6, Review7}negative class:{Review2, Review3}neutral class:{Review5}


Methodology - Architecture


Methodologytf-idf:

Inverted ConformityFrequency (ICF):

Uniformity (Uni):tf-idf > 26 ICF < log(2)Uni > 0.2


Methodology


Experiments - Data set and evaluation

• Data set: Reuter-21578 movie review e-book review

• Evaluation


Experiments (parameters)


Experiments


Experiments (conceptualization)


Experiments


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.


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

Presenter : Jian-Ren Chen Authors : Sheng-Tun Li a,b,* , Fu-Ching Tsai a 2013 , KBS

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