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
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
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