"{ Package: 'stx:libbasic2' }"

"{ NameSpace: Smalltalk }"

TextClassifier subclass:#BayesClassifier

	instanceVariableNames:''

	classVariableNames:''

	poolDictionaries:''

	category:'Collections-Text-Support'

!

!BayesClassifier class methodsFor:'documentation'!

documentation

"

    an initial experiment in bayes text classification.

    see BayesClassifierTest

    This is possibly unfinished and may need more work.

    [author:]

        cg

"

!

examples

"

    |b|

    b := BayesClassifier new.

    'teach it positive phrases'.

    b classify:'amazing, awesome movie!!!! Yeah!!!!' asCategory: 'positive'.

    b classify:'Sweet, this is incredibly, amazing, perfect, great!!!!' asCategory: 'positive'.

    'teach it a negative phrase'.

    b classify:'terrible, shitty thing. Damn. Sucks!!!!' asCategory: 'negative'.

    'teach it a neutral phrase'.

    b classify:'I dont really know what to make of this.' asCategory: 'neutral'.

    'now test it to see that it correctly categorizes a new document'.

    self assert:(b classify:'awesome, cool, amazing!!!! Yay.')= 'positive'.

"

! !

!BayesClassifier methodsFor:'text handling'!

classify:string

    "assume that it is a regular text.

     split first into lines..."

    |tokens frequencyTable maxProbability chosenCategory|

    maxProbability := Infinity negative.

    tokens := self tokenize:string.

    frequencyTable := tokens asBag.

    categories do:[:categoryName |

        |categoryProbability logProbability|

        categoryProbability := (docCounts at:categoryName) / docCounts size.

        logProbability := categoryProbability log.

        frequencyTable valuesAndCountsDo:[:token :frequencyInText |

            | tokenProbability|

            tokenProbability := self tokenProbabilityOf:token inCategory:categoryName.

            logProbability := logProbability + (frequencyInText * tokenProbability log).

        ].

        Transcript show:'P(',categoryName,') = '; showCR:logProbability.

        logProbability > maxProbability ifTrue:[

            maxProbability := logProbability.

            chosenCategory := categoryName.

        ].

    ].

    ^ chosenCategory

!

classify:string asCategory:categoryName

    |tokens frequencyTable sumWordCount|

    self initializeCategory:categoryName.

    docCounts incrementAt:categoryName.

    tokens := self tokenize:string.

    frequencyTable := tokens asBag.

    sumWordCount := 0.

    frequencyTable valuesAndCountsDo:[:token :count |

        vocabulary add:token.

        (wordFrequencyCounts at:categoryName) incrementAt:token by:count.

        sumWordCount := sumWordCount + count.

    ].

    wordCounts incrementAt:categoryName by:sumWordCount

!

tokenProbabilityOf:token inCategory:category

    "Calculate probability that a `token` belongs to a `category`"

    |wordFrequencyCount wordCount prob|

    wordFrequencyCount := (wordFrequencyCounts at:category) at:token ifAbsent:0.

    wordCount := wordCounts at:category.

    "/use laplace Add-1 Smoothing equation

    prob :=( wordFrequencyCount + 1 ) / ( wordCount + vocabulary size ).

    prob := prob asFloat.

    Transcript showCR:('  P(%1, %2) = %3' bindWith:token with:category with:prob).

    ^ prob

! !

!BayesClassifier class methodsFor:'documentation'!

version

    ^ '$Header$'

!

version_CVS

    ^ '$Header$'

! !