import ngramGenerator
import preprocessing
KERNEL_FUNCTION='linear'
C_PARAMETER=0.6
print "Initializing dictionnaries"
stopWords = preprocessing.getStopWordList('../resources/stopWords.txt')
slangs = preprocessing.loadSlangs('../resources/internetSlangs.txt')
afinn=polarity.loadAfinn('../resources/afinn.txt')
#sentiWordnet=polarity.loadSentiWordnet('../resources/sentiWordnetBig.csv')
emoticonDict=features.createEmoticonDictionary("../resources/emoticon.txt")
print "Bulding Bag of words ..."
positive=ngramGenerator.mostFreqList('../data/used/positive1.csv',3000)
negative=ngramGenerator.mostFreqList('../data/used/negative1.csv',3000)
neutral=ngramGenerator.mostFreqList('../data/used/neutral1.csv',3000)
for w in positive:
if w in negative+neutral :
positive.remove(w)
for w in negative:
if w in positive+neutral :
negative.remove(w)
for w in neutral:
if w in negative+positive :
neutral.remove(w)
import preprocessing
# User input for model parameters
N_NEIGHBORS = 10 # number of neighbors for KNN
KERNEL_FUNCTION = 'linear' # kernel function for SVM
C_PARAMETER = 0.2
UNIGRAM_SIZE = 3000
print "Initializing dictionnaries"
stopWords = preprocessing.getStopWordList('../resources/stopWords.txt')
slangs = preprocessing.loadSlangs('../resources/internetSlangs.txt')
afinn = polarity.loadAfinn('../resources/afinn.txt')
emoticonDict = features.createEmoticonDictionary("../resources/emoticon.txt")
print "Bulding unigram vector"
positive = ngramGenerator.mostFreqList('../data/used/positive1.csv',
UNIGRAM_SIZE) # add as needed
negative = ngramGenerator.mostFreqList('../data/used/negative1.csv',
UNIGRAM_SIZE)
neutral = ngramGenerator.mostFreqList('../data/used/neutral1.csv',
UNIGRAM_SIZE)
for w in positive:
if w in negative + neutral:
positive.remove(w)
for w in negative:
if w in positive + neutral:
negative.remove(w)
for w in neutral:
if w in negative + positive:
import preprocessing
import ngramGenerator
import polarity
import features
stopWords = preprocessing.getStopWordList('../resources/stopWords.txt')
slangs = preprocessing.loadSlangs('../resources/internetSlangs.txt')
sentiWordnet=polarity.loadSentiFull('../resources/sentiWordnetBig.csv')
emoticonDict=features.createEmoticonDictionary("../resources/emoticon.txt")
# do the preprocessing here and 3 output files
# done in the threeFileGen script
# define here lists of unigram for each file , 3 lists
pos=ngramGenerator.mostFreqList('../data/positive_processed.csv',2)
positive=[w[0] for w in pos]
neg=ngramGenerator.mostFreqList('../data/negative_processed.csv',2)
negative=[w[0] for w in neg]
neu=ngramGenerator.mostFreqList('../data/neutral_processed.csv',2)
neutral=[w[0] for w in neu]
total=positive+negative+neutral # total unigram vector
#print len(total)
# prepare mapping function
def mapper(filename,label):
# k=0
f=open(filename,'r')
line=f.readline()
import features
import polarity
import ngramGenerator
import preprocessing
#WEUGHTS_VECTOR=[1.0,1.0,0.6]
print "Initializing dictionnaries"
stopWords = preprocessing.getStopWordList('../resources/stopWords.txt')
slangs = preprocessing.loadSlangs('../resources/internetSlangs.txt')
afinn = polarity.loadAfinn('../resources/afinn.txt')
#sentiWordnet=polarity.loadSentiWordnet('../resources/sentiWordnetBig.csv')
emoticonDict = features.createEmoticonDictionary("../resources/emoticon.txt")
print "Bulding unigram vector"
positive = ngramGenerator.mostFreqList('../data/used/positive1.csv', 3000)
negative = ngramGenerator.mostFreqList('../data/used/negative1.csv', 3000)
neutral = ngramGenerator.mostFreqList('../data/used/neutral1.csv', 3000)
total = positive + negative + neutral # total unigram vector
for w in total:
count = total.count(w)
if (count > 1):
while (count > 0):
count = count - 1
total.remove(w)
# equalize unigrams sizes
m = min([len(positive), len(negative), len(neutral)])
positive = positive[0:m - 1]
negative = negative[0:m - 1]
# User input for model parameters
N_NEIGHBORS=10 # number of neighbors for KNN
KERNEL_FUNCTION='linear' # kernel function for SVM
C_PARAMETER=0.2
UNIGRAM_SIZE=3000
print "Initializing dictionnaries"
stopWords = preprocessing.getStopWordList('../resources/stopWords.txt')
slangs = preprocessing.loadSlangs('../resources/internetSlangs.txt')
afinn=polarity.loadAfinn('../resources/afinn.txt')
emoticonDict=features.createEmoticonDictionary("../resources/emoticon.txt")
print "Bulding unigram vector"
positive=ngramGenerator.mostFreqList('../data/used/positive1.csv',UNIGRAM_SIZE) # add as needed
negative=ngramGenerator.mostFreqList('../data/used/negative1.csv',UNIGRAM_SIZE)
neutral=ngramGenerator.mostFreqList('../data/used/neutral1.csv',UNIGRAM_SIZE)
for w in positive:
if w in negative+neutral :
positive.remove(w)
for w in negative:
if w in positive+neutral :
negative.remove(w)
for w in neutral:
if w in negative+positive :
neutral.remove(w)
import polarity
import ngramGenerator
import preprocessing
#WEUGHTS_VECTOR=[1.0,1.0,0.6]
print "Initializing dictionnaries"
stopWords = preprocessing.getStopWordList('../resources/stopWords.txt')
slangs = preprocessing.loadSlangs('../resources/internetSlangs.txt')
#sentiWordnet=polarity.loadSentiFull('../resources/sentiWordnetBig.csv')
sentiWordnet=polarity.loadSentiWordnet('../resources/sentiWordnetBig.csv')
emoticonDict=features.createEmoticonDictionary("../resources/emoticon.txt")
print "Bulding 150 unigram vector"
positive=ngramGenerator.mostFreqList('../data/positive_processed.csv',1000)
negative=ngramGenerator.mostFreqList('../data/negative_processed.csv',1000)
neutral=ngramGenerator.mostFreqList('../data/neutral_processed.csv',1000)
total=positive+negative+neutral # total unigram vector
for w in total:
count=total.count(w)
if (count > 1):
while (count>0):
count=count-1
total.remove(w)
def mapTweet(tweet,sentiWordnet,emoDict,unigram,slangs):
z_scaled = scaler.transform([z])
z = normalizer.transform(z_scaled)
z = z[0].tolist()
return model.predict([z]).tolist()
# Preprocesamiento de los archivos
stopWords = preprocessing.getStopWordList(
abs_file_url('resources/stopWords.txt'))
slangs = preprocessing.loadSlangs(abs_file_url('resources/internetSlangs.txt'))
afinn = polarity.loadAfinn(abs_file_url('resources/afinn.txt'))
emoticonDict = features.createEmoticonDictionary(
abs_file_url('resources/emoticon.txt'))
# Se construye el vector con las palabras más frecuentes presentes en tweets positivos, negativos, y neutrales
positive = ngramGenerator.mostFreqList(abs_file_url('data/used/positive1.csv'),
3000)
negative = ngramGenerator.mostFreqList(abs_file_url('data/used/negative1.csv'),
3000)
neutral = ngramGenerator.mostFreqList(abs_file_url('data/used/neutral1.csv'),
3000)
# Normalizamos el tamaño de los unigramas, si es que son menores a 3000
min_len = min([len(positive), len(negative), len(neutral)])
positive = positive[0:min_len]
negative = negative[0:min_len]
neutral = neutral[0:min_len]
# Cargamos los tweets de entrenamiento
# 4 = positivo
# 2 = neutral