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Automatic and dynamic profiling of enterprises
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Automatic and dynamic profiling of enterprises
1.
Dissertation supervised by
Prof. Ana Paula Rocha and Prof. António Castro LIACC
2.
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Context
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General
Financial Social • Name • Stock Price • Facebook Fans • Founded • Market Capitalisation • SocialBakers Total • Origin • Revenue Score • Employees • SocialBakers Fan • Sector Score • SocialBakers Content Score • SocialBakers Engagement Score • SocialBakers Warning Score • ReviewCentre Score • ReviewCentre Reviewers 14
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General
Financial Social • Name • Stock Price • Facebook Fans • Founded • Market Capitalisation • SocialBakers Total • Origin • Revenue Score • Employees • SocialBakers Fan • Sector Score • SocialBakers Content Score • SocialBakers Engagement Score • SocialBakers Warning Score • ReviewCentre Score • ReviewCentre Reviewers 15
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Frequency
C4.5 Increase Classification Tree Group Similarity Index Automatic Stereotype Extractor 18
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C4.5
SocialBakers GSI FIc Employees < SocialBakers Total Score > 500 000 attributes 48.9% Stock Price < Activity Sector 144 dollars SocialBakers Fan Score > 5% Activity Sector: IT, consumer goods or transportation 22
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Classification of new
Enterprises 23
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Classification
Algorithms Faster than • Multilayer Perceptron clustering • Sequential minimal optimization • k-nearest neighbors • Naive Bayes • C4.5 • Random Forest Missing • Radial Basis Values Function Network 24
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Classifiers
Test Dataset Initial Dataset Test Dataset Missing Missing Missing Values No Missing Values Missing Social Values Financial Values No Missing Values Missing Social Values Financial Values Best Algorithm Naïve Bayes RBF Network Naïve Bayes RBF Network Naïve Bayes RBF Network Error Rate 4% 17% 2% 2% 34% 3% 25
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A valid process
was defined for enterprise profiling A method capable of identifying the clusters meaning was created A software to automate the process was developed 34
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Hierarchical Clustering
More Enterprises More attributes 35
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