emolib.classifier.machinelearning
Class LSA

java.lang.Object
  emolib.util.proc.TextDataProcessor
      emolib.classifier.Classifier
          emolib.classifier.machinelearning.LSA

All Implemented Interfaces:

Configurable, DataProcessor

public class LSA
extends Classifier

The LSA class is a Latent Semantic Analysis classifier.

It learns the latent semantic space by computing the Singular Value Decomposition (SVD) of the term-class matrix (i.e., constructing a low-rank approximation with its principal eigenvectors). The cosine similarity between the class vectors (right singular vectors) and the query text vectors (obtained by adding the observed term vectors, i.e., the left singular vectors) is used to make decisions in the reduced latent space.

The core implementation of this LSA classifier is based on LingPipe. The same term weighting schemes as the ones used in the ARN-R are considered.

Author:

Alexandre Trilla (atrilla@salle.url.edu)

See Also:

ARNReduced

Constructor Summary

LSA()
Main constructor of this LSA classifier.

Method Summary

java.lang.String	getCategory(FeatureBox inputFeatures) The function that decides the most appropriate emotional category.
void	load(java.lang.String path) Generic function to load a previously saved classifier.
void	resetExamples() Method to reset the classifier and flush the training examples.
void	save(java.lang.String path) Generic method to save the fully fledged classifier into a given file path.
void	setCOF(boolean cof) Method to consider bigram frequencies.
void	setPOS(boolean pos) Method to consider POS tags.
void	setStemming(boolean stems) Method to consider stems.
void	setSynonyms(boolean syns) Method to consider synonyms.
void	setTermWeighingMeasure(java.lang.String twm) Method to set the TW method.
void	simpleClassification() Functionality test.
void	trainingProcedure() Training method based on the SVD decomposition.

Methods inherited from class emolib.classifier.Classifier

applyClassification, getData, getListOfExampleCategories, getListOfExampleFeatures, initialize, inputTrainingExample, newProperties, register, train

Methods inherited from class emolib.util.proc.TextDataProcessor

flush, getName, getPredecessor, setPredecessor, toString

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

LSA

public LSA()

Main constructor of this LSA classifier. The LSA object is actually initialised when the SVD matrix is constructed.

Method Detail

getCategory

public java.lang.String getCategory(FeatureBox inputFeatures)

Description copied from class: Classifier

The function that decides the most appropriate emotional category. This is required for any classifier. The classifier in question has to previously run any training algorithm in order to provide the required prediction.

Specified by:

getCategory in class Classifier

Parameters:

inputFeatures - The input emotional features.

Returns:

The most appropriate emotional category.

setPOS

public void setPOS(boolean pos)

Method to consider POS tags.

Parameters:

pos - The POS flag.

setStemming

public void setStemming(boolean stems)

Method to consider stems.

Parameters:

stems - The stemming flag.

setSynonyms

public void setSynonyms(boolean syns)

Method to consider synonyms.

Parameters:

syns - The synonyms flag.

trainingProcedure

public void trainingProcedure()

Training method based on the SVD decomposition.

Specified by:

trainingProcedure in class Classifier

setTermWeighingMeasure

public void setTermWeighingMeasure(java.lang.String twm)

Method to set the TW method.

Parameters:

twm - The TW method.

setCOF

public void setCOF(boolean cof)

Method to consider bigram frequencies.

Parameters:

cof - The COF flag.

save

public void save(java.lang.String path)

Description copied from class: Classifier

Generic method to save the fully fledged classifier into a given file path. It is recommended to use a plain text file (such as XML) to save the classifier's configuration since it's readable directly.

Specified by:

save in class Classifier

Parameters:

path - The file path to save the classifier.

load

public void load(java.lang.String path)

Description copied from class: Classifier

Generic function to load a previously saved classifier. This function should be consistent with the design followed in the saving procedure.

Specified by:

load in class Classifier

Parameters:

path - The path of the file which contains the previously saved classifier.

resetExamples

public void resetExamples()

Description copied from class: Classifier

Method to reset the classifier and flush the training examples. This method only makes sense if the classifier in question is trainable and already has some training examples.

Overrides:

resetExamples in class Classifier

simpleClassification

public void simpleClassification()

Functionality test.