Esempio n. 1
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def mapTweet(tweet, afinn, emoDict, positive, negative, neutral, slangs):
    out = []
    line = preprocessing.processTweet(
        tweet, stopWords, slangs
    )  # limpio el tweet, eliminando las palabras innecesarias y sobreescribiendo los modismos
    out.append(polarity.afinnPolarity(line, afinn))  # afinidad
    out.append(float(features.emoticonScore(line, emoDict)))  # emoticon score
    out.append(float(features.hashtagWordsRatio(
        line)))  # porcentaje de palabras con hashtag
    out.append(float(len(line) /
                     140))  # tamaño total de los 140 carácteres utilizados
    out.append(float(features.upperCase(
        line)))  # si existen mayúsuculas en el tweet; 1 = si, 0 = no
    out.append(float(features.exclamationTest(
        line)))  # si tiene signo de exclamación o no; 1 = si, 0 = no
    out.append(float(line.count("!") /
                     140))  # procentaje de signos de exlamación
    out.append(float(
        (features.questionTest(line))))  # si tiene un signo de pregunta
    out.append(float(line.count('?') /
                     140))  # procentaje de signos de preguntas
    out.append(float(
        features.freqCapital(line)))  # porcentaje de las letras en mayusculas
    u = features.scoreUnigram(
        line, positive, negative, neutral
    )  # Score sobre el vector de palabras utilizadas en los documentos de prueba
    out.extend(u)
    return out
Esempio n. 2
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File: svm.py Progetto: ziany/uranus
def mapTweet(tweet,afinn,emoDict,positive,negative,neutral,slangs):
    out=[]
    line=preprocessing.processTweet(tweet,stopWords,slangs)
    p=polarity.afinnPolarity(line,afinn)
    out.append(p)
    out.append(float(features.emoticonScore(line,emoDict))) # emo aggregate score be careful to modify weights
    out.append(float(len(features.hashtagWords(line))/40)) # number of hashtagged words
    out.append(float(len(line)/140)) # for the length
    out.append(float(features.upperCase(line))) # uppercase existence : 0 or 1
    out.append(float(features.exclamationTest(line)))
    out.append(float(line.count("!")/140))
    out.append(float((features.questionTest(line))))
    out.append(float(line.count('?')/140))
    out.append(float(features.freqCapital(line)))
    u=features.scoreUnigram(line,positive,negative,neutral)
    out.extend(u)
    return out
Esempio n. 3
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def mapTweet(tweet,afinn,emoDict,positive,negative,neutral,slangs):
    out=[]
    line=preprocessing.processTweet(tweet,stopWords,slangs)
    p=polarity.afinnPolarity(line,afinn)
    out.append(p)
    out.append(float(features.emoticonScore(line,emoDict))) # emo aggregate score be careful to modify weights
    out.append(float(len(features.hashtagWords(line))/40)) # number of hashtagged words
    out.append(float(len(line)/140)) # for the length
    out.append(float(features.upperCase(line))) # uppercase existence : 0 or 1
    out.append(float(features.exclamationTest(line)))
    out.append(float(line.count("!")/140))
    out.append(float((features.questionTest(line))))
    out.append(float(line.count('?')/140))
    out.append(float(features.freqCapital(line)))
    u=features.scoreUnigram(line,positive,negative,neutral)
    out.extend(u)
    return out
Esempio n. 4
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File: svm.py Progetto: ziany/neptune
def mapTweet(tweet,sentiWordnet,emoDict,positive,negative,neutral,slangs):
    out=[]
    line=preprocessing.processTweet(tweet,stopWords,slangs)
   
    p=polarity.posPolarity(line,sentiWordnet)
    out.extend([p[0],p[1],p[2]/2]) # aggregate polsarity pos - negative
#    out.extend(p[7:]) # frequencies of pos 
#    out.append(float(features.emoticonScore(line,emoDict))) # emo aggregate score be careful to modify weights
#    out.append(float(len(features.hashtagWords(line))/40)) # number of hashtagged words
#    out.append(float(len(line)/140)) # for the length
#    out.append(float(features.upperCase(line))) # uppercase existence : 0 or 1
#    out.append(float(features.exclamationTest(line)))
#    out.append(float(line.count("!")/140))
#    out.append(float((features.questionTest(line))))
#    out.append(float(line.count('?')/140))
#    out.append(float(features.freqCapital(line)))
    u=features.scoreUnigram(tweet,positive,negative,neutral)
    out.extend(u)
    return out
Esempio n. 5
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def mapTweet(tweet,sentiWordnet,emoDict,positive,negative,neutral,slangs):
    out=[]
    line=preprocessing.processTweet(tweet,stopWords,slangs)
   
#    p=polarity.polarity(line,sentiWordnet)
    p=polarity.posPolarity(line,sentiWordnet)
   
    out.extend([float(p[0]),float(p[1]),float(p[2])]) # aggregate polarity for pos neg and neutral here neutral is stripped
#    pos=polarity.posFreq(line,sentiWordnet)
    out.extend(p[3:]) # frequencies of pos 

#    out.extend([float(pos['v']),float(pos['n']),float(pos['a']),float(pos['r'])]) # pos counts inside the tweet
    out.append(float(features.emoticonScore(line,emoDict))) # emo aggregate score be careful to modify weights
    out.append(float(len(features.hashtagWords(line))/40)) # number of hashtagged words
    out.append(float(len(line)/140)) # for the length
    out.append(float(features.upperCase(line))) # uppercase existence : 0 or 1
    out.append(float(features.exclamationTest(line)))
    out.append(float(line.count("!")/140))
    out.append(float((features.questionTest(line))))
    out.append(float(line.count('?')/140))
    out.append(float(features.freqCapital(line)))
    u=features.scoreUnigram(tweet,positive,negative,neutral)
    out.extend(u)
    return out