def frequency_selector():
dumpTwittes('sentiment.csv', 'positive_twittes_dump.csv', 'positive')
dumpTwittes('sentiment.csv', 'negative_twittes_dump.csv', 'negative')
dumpTwittes('sentiment.csv', 'neutral_twittes_dump.csv', 'neutral')
dumpTwittes('sentiment.csv', 'neutral_twittes_dump.csv', 'irrelevant')
positive_words = dict(count_freq('positive_twittes_dump.csv', 1000))
negative_words = dict(count_freq('negative_twittes_dump.csv', 1000))
neutral_words = dict(count_freq('neutral_twittes_dump.csv', 1000))
popular_control = 50
threshold = 100
positive_features = {}
for word in positive_words:
if (word in negative_words and (negative_words[word] > popular_control or positive_words[word] < negative_words[word] * threshold)) \
or (word in neutral_words and (neutral_words[word] > popular_control or positive_words[word] < neutral_words[word] * threshold)):
continue
positive_features[word] = positive_words[word]
positive_features = sorted(positive_features.iteritems(),
key=lambda (word, count): (-count, word))[:100]
# print positive_features
negative_features = {}
for word in negative_words:
if (word in positive_words and (positive_words[word] > popular_control or negative_words[word] < positive_words[word] * threshold)) \
or (word in neutral_words and (neutral_words[word] > popular_control or negative_words[word] < neutral_words[word] * threshold)):
continue
negative_features[word] = negative_words[word]
negative_features = sorted(negative_features.iteritems(),
key=lambda (word, count): (-count, word))[:100]
neutral_features = {}
for word in neutral_words:
if (word in negative_words and (negative_words[word] > popular_control or neutral_words[word] < negative_words[word] * threshold)) \
or (word in positive_words and (positive_words[word] > popular_control or neutral_words[word] < positive_words[word] * threshold)):
continue
neutral_features[word] = neutral_words[word]
neutral_features = sorted(neutral_features.iteritems(),
key=lambda (word, count): (-count, word))[:100]
spamWriter = csv.writer(open('postive_features.csv', 'w'),
delimiter=',',
quotechar='"')
spamWriter.writerow(['***postive***', '***MI***'])
for word, freq in positive_features:
spamWriter.writerow([word, freq])
spamWriter = csv.writer(open('negative_features.csv', 'w'),
delimiter=',',
quotechar='"')
spamWriter.writerow(['***negative***', '***MI***'])
for word, freq in negative_features:
spamWriter.writerow([word, freq])
spamWriter = csv.writer(open('neutral_features.csv', 'w'),
delimiter=',',
quotechar='"')
spamWriter.writerow(['***neutral***', '***MI***'])
for word, freq in neutral_features:
spamWriter.writerow([word, freq])
def frequency_selector():
dumpTwittes('sentiment.csv', 'positive_twittes_dump.csv', 'positive')
dumpTwittes('sentiment.csv', 'negative_twittes_dump.csv', 'negative')
dumpTwittes('sentiment.csv', 'neutral_twittes_dump.csv', 'neutral')
dumpTwittes('sentiment.csv', 'neutral_twittes_dump.csv', 'irrelevant')
positive_words = dict(count_freq('positive_twittes_dump.csv', 1000))
negative_words = dict(count_freq('negative_twittes_dump.csv', 1000))
neutral_words = dict(count_freq('neutral_twittes_dump.csv', 1000))
popular_control = 50
threshold = 100
positive_features = {}
for word in positive_words:
if (word in negative_words and (negative_words[word] > popular_control or positive_words[word] < negative_words[word] * threshold)) \
or (word in neutral_words and (neutral_words[word] > popular_control or positive_words[word] < neutral_words[word] * threshold)):
continue
positive_features[word] = positive_words[word]
positive_features = sorted(positive_features.iteritems(),
key=lambda(word, count): (-count, word))[:100]
# print positive_features
negative_features = {}
for word in negative_words:
if (word in positive_words and (positive_words[word] > popular_control or negative_words[word] < positive_words[word] * threshold)) \
or (word in neutral_words and (neutral_words[word] > popular_control or negative_words[word] < neutral_words[word] * threshold)):
continue
negative_features[word] = negative_words[word]
negative_features = sorted(negative_features.iteritems(),
key=lambda(word, count): (-count, word))[:100]
neutral_features = {}
for word in neutral_words:
if (word in negative_words and (negative_words[word] > popular_control or neutral_words[word] < negative_words[word] * threshold)) \
or (word in positive_words and (positive_words[word] > popular_control or neutral_words[word] < positive_words[word] * threshold)):
continue
neutral_features[word] = neutral_words[word]
neutral_features = sorted(neutral_features.iteritems(),
key=lambda(word, count): (-count, word))[:100]
spamWriter = csv.writer(open('postive_features.csv', 'w'), delimiter=',', quotechar='"')
spamWriter.writerow(['***postive***', '***MI***'])
for word, freq in positive_features:
spamWriter.writerow([word, freq])
spamWriter = csv.writer(open('negative_features.csv', 'w'), delimiter=',', quotechar='"')
spamWriter.writerow(['***negative***', '***MI***'])
for word, freq in negative_features:
spamWriter.writerow([word, freq])
spamWriter = csv.writer(open('neutral_features.csv', 'w'), delimiter=',', quotechar='"')
spamWriter.writerow(['***neutral***', '***MI***'])
for word, freq in neutral_features:
spamWriter.writerow([word, freq])
def feature_selection(category, frequency, stat):
top_words = count_freq('sentiment.csv', frequency)
print '\n\n\n'
print top_words
print '\n\n\n'
mi_result = {}
mi_result = defaultdict(int)
for word, frequency in top_words:
result = mi_oneword(word, category, stat)
if result > 0:
mi_result[word] = result
# if(stat==MI):
# if(mi != -11 and mi != -10 and mi != 0 and mi != -1):
# mi_result[word] = mi;
# elif(stat==CHI):
# if(mi !=-12 and mi != -21 and mi != -2 and mi != -20):
# mi_result[word] = mi;
print word, ':', mi_result[word]
#print mi_oneword('the', 'positive')
feature = {}
feature = sorted(mi_result.iteritems(),
key=lambda (word, count):
(-count, word))[:len(mi_result) - 1]
return feature
def feature_selection(category, frequency, stat):
top_words = count_freq.count_freq('sentiment.csv', frequency)
print '\n\n\n'
print top_words
print '\n\n\n'
mi_result = {}
mi_result = defaultdict(int)
for word, frequency in top_words:
result = mi_oneword(word, category, stat)
if result > 0:
mi_result[word] = result
# if(stat==MI):
# if(mi != -11 and mi != -10 and mi != 0 and mi != -1):
# mi_result[word] = mi;
# elif(stat==CHI):
# if(mi !=-12 and mi != -21 and mi != -2 and mi != -20):
# mi_result[word] = mi;
print word, ':', mi_result[word]
#print mi_oneword('the', 'positive')
feature = {}
feature = sorted(mi_result.iteritems(), key=lambda(word, count) : (-count, word))[:len(mi_result) - 1]
return feature
