def extractFeatureVocab(tweets, keyword="all", usephrasemodel=True, phrasemodel="phrase.model", anon_targets=False):
tokencounts = Counter()
features_final = []
bigram = Phrases(phrasemodel)
#tokens_topic = []
#if keyword == "all":
# for top in tokenize_tweets.TOPICS:
# if top != 'clinton':
# for tok in tokenize(tokenize_tweets.TOPICS_LONG[top]):
# tokens_topic.append(tok)
#else:
# tokens_topic = tokenize(tokenize_tweets.TOPICS_LONG[keyword])
for tweet in tweets:
if usephrasemodel == False:
tokenised_tweet = tokenize(tweet)
for token in tokenised_tweet: #unigram features
tokencounts[token] += 1
#for toktopic in tokens_topic:
# tokencounts[toktopic + '|' + token] += 1
for l in zip(*[tokenised_tweet[i:] for i in range(2)]): #bigram features
tokencounts["_".join(l)] += 1
#for ltop in zip(*[tokens_topic[i:] for i in range(2)]):
# tokencounts["_".join(ltop) + '|' + "_".join(l)] += 1
else:
# this includes unigrams and frequent bigrams
tokens = filterStopwords(tokenize(tweet.lower())) #For Trump it's [1]
phrasetoks = bigram[tokens]
target_keywords = []
if anon_targets==True:
for top in tokenize_tweets.TOPICS:
if top == "climate": # hack, this is the only non-list value
target_keywords.append("climate")
else:
#for keyw in tokenize_tweets.KEYWORDS[top]:
target_keywords.extend(tokenize_tweets.KEYWORDS[top])
phrasetoks_new = []
for token in phrasetoks:
for keyw in target_keywords:
if keyw in token:
token = token.replace(keyw, "TARGET")
phrasetoks_new.append(token)
phrasetoks = phrasetoks_new
for token in phrasetoks:
tokencounts[token] += 1
for l in zip(*[phrasetoks[i:] for i in range(2)]):
tokencounts["_".join(l)] += 1
for token, count in tokencounts.most_common():
if count > 1:
features_final.append(token)
#print token, count
return features_final
def findTokensPhrases(phrasemodel="phrase.model", useDev=False):
tokencnt = Counter()
bigram = Phrases(phrasemodel)
twcntr = 0
supercntr = 0
trumpcntr = 0
for line in open(INPUT, 'r'):
twcntr += 1
tokenised = tokenize(json.loads(line)['text'].lower())
tokens = filterStopwords(tokenised) # filter stopwords
for token in bigram[tokens]: # calling the phrase model, this leaves some as single tokens and feq occurring ones as bigrams
tokencnt[token] += 1
for line in io.open(tokenize_tweets.FILETRAIN, encoding='windows-1252', mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
tokens = filterStopwords(tokenize(line.split("\t")[2].lower())) #For Trump it's [1]
for token in bigram[tokens]:
supercntr += 1
tokencnt[token] += 1
if useDev == True:
for line in io.open(tokenize_tweets.FILEDEV, encoding='windows-1252', mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
tokens = filterStopwords(tokenize(line.split("\t")[2].lower())) #For Trump it's [1]
for token in bigram[tokens]:
supercntr += 1
tokencnt[token] += 1
for line in io.open(tokenize_tweets.FILETRUMP, encoding='utf-8', mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
tokens = filterStopwords(tokenize(line.split("\t")[1].lower())) #For Trump it's [1]
for token in bigram[tokens]:
trumpcntr += 1
tokencnt[token] += 1
output = open(OUTPUT, "wb")
tokens_pb = Tokens()
for token, count in tokencnt.most_common():
if count > 1: # not even worth saving singletons
token_pb = tokens_pb.tokens.add()
token_pb.token = token
token_pb.count = count
print "Saving token counts for ", tokencnt.__sizeof__(), ". ", twcntr, " unlabelled tweets, ", trumpcntr, " Donald Trump tweets, ", supercntr, " labelled tweets"
output.write(tokens_pb.SerializeToString())
output.close
def extractFeaturesBOW(tweets,
targets,
features_final,
anon_targets=False,
usephrasemodel=False,
phrasemodel="phrase.model"):
bigram = Phrases(phrasemodel)
matrix = [] # np.zeros((len(features_final), len(tweets)))
for i, tweet in enumerate(tweets):
vect = np.zeros((len(features_final)))
if usephrasemodel == False:
tokenised_tweet = tokenize(tweet)
for token in tokenised_tweet:
insertIntoVect(features_final, vect, token)
#for toktopic in tokens_topic:
# insertIntoVect(features_final, vect, toktopic + '|' + token)
for l in zip(*[tokenised_tweet[i:] for i in range(2)]):
insertIntoVect(features_final, vect, "_".join(l))
#for ltop in zip(*[tokens_topic[i:] for i in range(2)]):
# insertIntoVect(features_final, vect, "_".join(ltop) + '|' + "_".join(l))
else:
inv_topics = {v: k for k, v in tokenize_tweets.TOPICS_LONG.items()}
target_keywords = tokenize_tweets.KEYWORDS.get(
inv_topics.get(targets[i]))
tokens = filterStopwords(tokenize(
tweet.lower())) #For Trump it's [1]
phrasetoks = bigram[tokens]
if anon_targets == True:
phrasetoks_new = []
for token in phrasetoks:
if target_keywords == "climate":
if target_keywords in token:
token = token.replace(keyw, "TARGET")
else:
for keyw in target_keywords:
if keyw in token:
token = token.replace(keyw, "TARGET")
phrasetoks_new.append(token)
phrasetoks = phrasetoks_new
for token in phrasetoks:
insertIntoVect(features_final, vect, token)
for l in zip(*[phrasetoks[i:] for i in range(2)]):
insertIntoVect(features_final, vect, "_".join(l))
matrix.append(vect)
#print " ".join(str(v) for v in vect), "\n"
return matrix
def extractFeaturesBOW(tweets, targets, features_final, anon_targets=False, usephrasemodel=False, phrasemodel="phrase.model"):
bigram = Phrases(phrasemodel)
matrix = [] # np.zeros((len(features_final), len(tweets)))
for i, tweet in enumerate(tweets):
vect = np.zeros((len(features_final)))
if usephrasemodel == False:
tokenised_tweet = tokenize(tweet)
for token in tokenised_tweet:
insertIntoVect(features_final, vect, token)
#for toktopic in tokens_topic:
# insertIntoVect(features_final, vect, toktopic + '|' + token)
for l in zip(*[tokenised_tweet[i:] for i in range(2)]):
insertIntoVect(features_final, vect, "_".join(l))
#for ltop in zip(*[tokens_topic[i:] for i in range(2)]):
# insertIntoVect(features_final, vect, "_".join(ltop) + '|' + "_".join(l))
else:
inv_topics = {v: k for k, v in tokenize_tweets.TOPICS_LONG.items()}
target_keywords = tokenize_tweets.KEYWORDS.get(inv_topics.get(targets[i]))
tokens = filterStopwords(tokenize(tweet.lower())) #For Trump it's [1]
phrasetoks = bigram[tokens]
if anon_targets==True:
phrasetoks_new = []
for token in phrasetoks:
if target_keywords == "climate":
if target_keywords in token:
token = token.replace(keyw, "TARGET")
else:
for keyw in target_keywords:
if keyw in token:
token = token.replace(keyw, "TARGET")
phrasetoks_new.append(token)
phrasetoks = phrasetoks_new
for token in phrasetoks:
insertIntoVect(features_final, vect, token)
for l in zip(*[phrasetoks[i:] for i in range(2)]):
insertIntoVect(features_final, vect, "_".join(l))
matrix.append(vect)
#print " ".join(str(v) for v in vect), "\n"
return matrix
def countHashTags(tweets, labels):
neut = Counter()
neg = Counter()
pos = Counter()
all = Counter()
for it, tweet in enumerate(tweets):
tokenised_tweet = tokenize(tweet)
label = labels[it]
for token in tokenised_tweet:
if token.startswith("#"):
all[token] += 1
if label == "NONE":
neut[token] += 1
elif label == "AGAINST":
neg[token] += 1
elif label == "FAVOR":
pos[token] += 1
print("Hashtags\tAll\tNeut\tNeg\tPos")
for token, count in all.most_common():
neutrcnt, poscnt, negcnt = 0, 0, 0
if neut.__contains__(token):
neutrcnt = neut[token]
if neg.__contains__(token):
negcnt = neg[token]
if pos.__contains__(token):
poscnt = pos[token]
print(token, "\t", count, "\t", neutrcnt, "\t", negcnt, "\t", poscnt)
def writeToksToFile():
tokens,tweets_on_topic,tweets = readToks()
for topic in TOPICS:
tokenized_tweets = Tweets()
for index in tweets_on_topic[topic]:
tweet = tweets[index]
tokenized = tokenized_tweets.tweets.add()
tokenized.tweet = tweet['text']
for token in tokenize(tweet['text']):
try:
index = tokens.index(token)
tokenized.tokens.append(index)
except ValueError:
tokenized.tokens.append(-1)
print(tokenized.tokens)
f = open(topic + '.tweets', "wb")
f.write(tokenized_tweets.SerializeToString())
f.close()
def countHashTags(tweets, labels):
neut = Counter()
neg = Counter()
pos = Counter()
all = Counter()
for it, tweet in enumerate(tweets):
tokenised_tweet = tokenize(tweet)
label = labels[it]
for token in tokenised_tweet:
if token.startswith("#"):
all[token] += 1
if label == "NONE":
neut[token] += 1
elif label == "AGAINST":
neg[token] += 1
elif label == "FAVOR":
pos[token] += 1
print "Hashtags\tAll\tNeut\tNeg\tPos"
for token, count in all.most_common():
neutrcnt, poscnt, negcnt = 0, 0, 0
if neut.__contains__(token):
neutrcnt = neut[token]
if neg.__contains__(token):
negcnt = neg[token]
if pos.__contains__(token):
poscnt = pos[token]
print token, "\t", count, "\t", neutrcnt, "\t", negcnt, "\t", poscnt
def prepData(stopfilter, multiword, useDev=False):
print("Preparing data...")
ret = [] # list of lists
print("Reading data...")
tweets = readTweets()
tweets_train, targets_train, labels_train = readTweetsOfficial(tokenize_tweets.FILETRAIN, 'windows-1252', 2)
tweets_trump, targets_trump, labels_trump = readTweetsOfficial(tokenize_tweets.FILETRUMP, 'utf-8', 1)
print(str(len(tweets)))
tweets.extend(tweets_train)
print(str(len(tweets_train)), "\t" , str(len(tweets)))
tweets.extend(tweets_trump)
print(str(len(tweets_trump)), "\t" , str(len(tweets)))
if useDev == True:
tweets_dev, targets_dev, labels_dev = readTweetsOfficial(tokenize_tweets.FILEDEV, 'windows-1252', 2)
tweets.extend(tweets_dev)
print(str(len(tweets_dev)), "\t" , str(len(tweets)))
print("Tokenising...")
for tweet in tweets:
tokenised_tweet = tokenize(tweet.lower())
if stopfilter:
words = filterStopwords(tokenised_tweet)
ret.append(words)
else:
ret.append(tokenised_tweet)
if multiword:
return learnMultiword(ret)
else:
return ret
def prepData(filepath, stopfilter, multiword):
print("Preparing data...")
ret = [] # list of lists
print("Reading data...")
# this reads file in JSON format
#tweets = readTweets(jsonfilepath)
# this reads SemEval format tweets
tweets, _, _, _ = readTweetsOfficial(filepath)
#tweets = "\n".join(tweets)
print("Tokenising...")
for tweet in tweets:
tokenised_tweet = tokenize(tweet.lower())
if stopfilter:
words = filterStopwords(tokenised_tweet)
ret.append(words)
else:
ret.append(tokenised_tweet)
if multiword:
return learnMultiword(ret)
else:
return ret
def extractW2VAggrFeatures(w2vmodel, phrasemodel, tweets, targets, labels):
feats = []
# for each tweet, multiply the word vectors
for i, tweet in enumerate(tweets):
tokenised_tweet = tokenize(tweet.lower())
words = filterStopwords(tokenised_tweet)
numvects = 0
vect = []
for token in phrasemodel[words]:
try:
s = w2vmodel[token]
vect.append(s)
numvects += 1
except KeyError:
s = 0.0
if vect.__len__() > 0:
mtrmean = np.average(vect, axis=0)
if i == 0:
feats = mtrmean
else:
feats = np.vstack((feats, mtrmean))
else:
feats = np.vstack(
(feats, np.zeros(300))) # 300-dimensional vector for now
return feats
def prepData(stopfilter, multiword, useDev=False):
print("Preparing data...")
ret = [] # list of lists
print("Reading data...")
tweets = readTweets()
tweets_train, targets_train, labels_train = readTweetsOfficial(
tokenize_tweets.FILETRAIN, 'windows-1252', 2)
tweets_trump, targets_trump, labels_trump = readTweetsOfficial(
tokenize_tweets.FILETRUMP, 'utf-8', 1)
print(str(len(tweets)))
tweets.extend(tweets_train)
print(str(len(tweets_train)), "\t", str(len(tweets)))
tweets.extend(tweets_trump)
print(str(len(tweets_trump)), "\t", str(len(tweets)))
if useDev == True:
tweets_dev, targets_dev, labels_dev = readTweetsOfficial(
tokenize_tweets.FILEDEV, 'windows-1252', 2)
tweets.extend(tweets_dev)
print(str(len(tweets_dev)), "\t", str(len(tweets)))
print("Tokenising...")
for tweet in tweets:
tokenised_tweet = tokenize(tweet.lower())
if stopfilter:
words = filterStopwords(tokenised_tweet)
ret.append(words)
else:
ret.append(tokenised_tweet)
if multiword:
return learnMultiword(ret)
else:
return ret
def extractW2VAggrFeatures(w2vmodel, phrasemodel, tweets, targets, labels):
feats = []
# for each tweet, multiply the word vectors
for i, tweet in enumerate(tweets):
tokenised_tweet = tokenize(tweet.lower())
words = filterStopwords(tokenised_tweet)
numvects = 0
vect = []
for token in phrasemodel[words]:
try:
s = w2vmodel[token]
vect.append(s)
numvects += 1
except KeyError:
s = 0.0
if vect.__len__() > 0:
mtrmean = np.average(vect, axis=0)
if i == 0:
feats = mtrmean
else:
feats = np.vstack((feats, mtrmean))
else:
feats = np.vstack((feats, np.zeros(300))) # 300-dimensional vector for now
return feats
def writeToksToFile():
tokens, tweets_on_topic, tweets = readToks()
for topic in TOPICS:
tokenized_tweets = Tweets()
for index in tweets_on_topic[topic]:
tweet = tweets[index]
tokenized = tokenized_tweets.tweets.add()
tokenized.tweet = tweet['text']
for token in tokenize(tweet['text']):
try:
index = tokens.index(token)
tokenized.tokens.append(index)
except ValueError:
tokenized.tokens.append(-1)
print(tokenized.tokens)
f = open(topic + '.tweets', "wb")
f.write(tokenized_tweets.SerializeToString())
f.close()
def main(tweet_fp):
pos_tagger = mallet_wrapper.MalletPOSTagger(_MODEL_FP, _TOKEN2POS_MAPS,
_TOKEN_MAPS, _BIGRAM,
_TEMP_DIR)
tweet_tokens_list = []
sys.stderr.write('Creating mallet test file.\n')
for line in open(tweet_fp):
tweet_tokens_list.append(twokenize_wrapper.tokenize(line.rstrip('\n')))
return pos_tagger.pos_tag_tweets(tweet_tokens_list)
def main(tweet_fp):
pos_tagger = mallet_wrapper.MalletPOSTagger(_MODEL_FP, _TOKEN2POS_MAPS,
_TOKEN_MAPS, _BIGRAM,
_TEMP_DIR)
tweet_tokens_list = []
sys.stderr.write('Creating mallet test file.\n')
for line in open(tweet_fp):
tweet_tokens_list.append(twokenize_wrapper.tokenize(line.rstrip('\n')))
return pos_tagger.pos_tag_tweets(tweet_tokens_list)
def convertTweetsOfficialToVec(numtoks,
tokens,
tweets,
filtering=False,
phrasemodelpath="phrase.model"):
tokens_sub = tokens[:numtoks]
tokenized_tweets = Tweets()
vects = []
norm_tweets = []
if filtering == True:
bigram = Phrases(phrasemodelpath)
for tweet in tweets:
vect = np.zeros(
numtoks
) # dimensionality. the most frequent tokens have a low index, then we can do a cutoff. original: 93988
norm_tweet = []
tokenized = tokenized_tweets.tweets.add()
tokenized.tweet = tweet
if filtering == False:
tokenised_tweet = tokenize(tokenized.tweet)
else:
tokens = filterStopwords(tokenize(tokenized.tweet.lower()))
tokenised_tweet = bigram[tokens]
for token in tokenised_tweet:
try:
index = tokens_sub.index(token)
except ValueError:
index = -1
if index > -1:
vect[index] = 1
norm_tweet.append(token)
else:
norm_tweet.append('NULL')
#print(norm_tweet)
norm_tweets.append(norm_tweet)
vects.append(vect)
return vects, norm_tweets
def findTokensAll():
tokens = Counter()
twcntr = 0
supercntr = 0
trumpcntr = 0
for line in open(INPUT, 'r'):
twcntr += 1
for token in tokenize(json.loads(line)['text']):
tokens[token] += 1
for line in io.open(tokenize_tweets.FILETRAIN,
encoding='windows-1252',
mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
for token in tokenize(line.split("\t")[2]): #For Trump it's [1]
supercntr += 1
tokens[token] += 1
for line in io.open(tokenize_tweets.FILETRUMP, encoding='utf-8',
mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
for token in tokenize(line.split("\t")[1]): #For Trump it's [1]
trumpcntr += 1
tokens[token] += 1
output = open(OUTPUT, "wb")
tokens_pb = Tokens()
for token, count in tokens.most_common():
if count > 1: # not even worth saving singletons
token_pb = tokens_pb.tokens.add()
token_pb.token = token
token_pb.count = count
print("Saving token counts for ", tokens.__sizeof__(), ". ", twcntr,
" unlabelled tweets, ", trumpcntr, " Donald Trump tweets, ",
supercntr, " labelled tweets")
output.write(tokens_pb.SerializeToString())
output.close
def cleanHelper(self, body):
tokens = tokenize(body)
tokens = [x.lower().strip() for x in tokens]
tokens = [x for x in tokens if emoticons(x) == "NA"]
tokens = [x.strip(" #\-*!._(){}~,^") for x in tokens]
tokens = [self.normalizer.replace(x) for x in tokens]
tokens = [x for x in tokens if re.search("\w", x)]
body = " ".join(tokens)
return tokens, body
def findTokensAll():
tokens = Counter()
twcntr = 0
supercntr = 0
trumpcntr = 0
for line in open(INPUT, 'r'):
twcntr += 1
for token in tokenize(json.loads(line)['text']):
tokens[token] += 1
for line in io.open(tokenize_tweets.FILETRAIN, encoding='windows-1252', mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
for token in tokenize(line.split("\t")[2]): #For Trump it's [1]
supercntr += 1
tokens[token] += 1
for line in io.open(tokenize_tweets.FILETRUMP, encoding='utf-8', mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
for token in tokenize(line.split("\t")[1]): #For Trump it's [1]
trumpcntr += 1
tokens[token] += 1
output = open(OUTPUT, "wb")
tokens_pb = Tokens()
for token, count in tokens.most_common():
if count > 1: # not even worth saving singletons
token_pb = tokens_pb.tokens.add()
token_pb.token = token
token_pb.count = count
print "Saving token counts for ", tokens.__sizeof__(), ". ", twcntr, " unlabelled tweets, ", trumpcntr, " Donald Trump tweets, ", supercntr, " labelled tweets"
output.write(tokens_pb.SerializeToString())
output.close
def convertTweetsOfficialToVec(numtoks, tokens, tweets, filtering=False, phrasemodelpath="phrase.model"):
tokens_sub = tokens[:numtoks]
tokenized_tweets = Tweets()
vects = []
norm_tweets = []
if filtering==True:
bigram = Phrases(phrasemodelpath)
for tweet in tweets:
vect = np.zeros(numtoks) # dimensionality. the most frequent tokens have a low index, then we can do a cutoff. original: 93988
norm_tweet = []
tokenized = tokenized_tweets.tweets.add()
tokenized.tweet = tweet
if filtering == False:
tokenised_tweet = tokenize(tokenized.tweet)
else:
tokens = filterStopwords(tokenize(tokenized.tweet.lower()))
tokenised_tweet = bigram[tokens]
for token in tokenised_tweet:
try:
index = tokens_sub.index(token)
except ValueError:
index = -1
if index > -1:
vect[index] = 1
norm_tweet.append(token)
else:
norm_tweet.append('NULL')
#print(norm_tweet)
norm_tweets.append(norm_tweet)
vects.append(vect)
return vects,norm_tweets
def thinposts(lines):
for line in lines:
m = re.search(subreddit_re, line)
if not m:
continue
comment = json.loads(line)
if comment['body'] == '[deleted]':
continue
if comment['subreddit'].lower() in subreddits:
reformed_text = ' '.join(twokenize_wrapper.tokenize(comment['body']))
yield reformed_text.strip() + ' <EOS>'
def thinposts(lines):
posts = []
for line in lines:
# if not re.search(subreddit_re, line):
# continue
comment = json.loads(line)
if comment['text'] == '[deleted]':
continue
if comment['community'][1]['name'].lower() in subreddits:
tokens = twokenize_wrapper.tokenize(comment['text'].strip())
yield ' '.join(tokens) + ' <EOS> '
def extractW2VFeaturesSim(w2vmodelfile, phrasemodel, tweets, targets, labels):
phmodel = Phrases.load(phrasemodel)
w2vmodel = word2vec.Word2Vec.load(w2vmodelfile)
inv_topics = {v: k for k, v in tokenize_tweets.TOPICS_LONG.items()}
for i, tweet in enumerate(tweets):
# get the neut/pos/neg hashtags
neut = KEYWORDS_NEUT[inv_topics[targets[i]]]
pos = KEYWORDS_POS[inv_topics[targets[i]]]
neg = KEYWORDS_NEG[inv_topics[targets[i]]]
tokenised_tweet = tokenize(tweet.lower())
words = filterStopwords(tokenised_tweet)
neutcnt, poscnt, negcnt = 0, 0, 0
neutsc, possc, negsc = 0.0, 0.0, 0.0
# transform, as earlier, with the phrase model
for token in phmodel[words]:
try:
neutsim = w2vmodel.similarity(neut, token)
neutcnt += 1
neutsc += neutsim
except KeyError:
neutsim = 0
try:
possim = w2vmodel.similarity(pos, token)
possc += possim
poscnt += 1
except KeyError:
possim = 0
try:
negsim = w2vmodel.similarity(neg, token)
negsc += negsim
negcnt += 1
except KeyError:
negsim = 0
#print targets[i], "\t", token, "\t", neutsim, "\t", possim, "\t", negsim
neutsc_tweet = neutsc/neutcnt
possc_tweet = possc/poscnt
negsc_tweet = negsc/negcnt
print(targets[i], "\t", labels[i], "\t", neutsc_tweet, "\t", possc_tweet, "\t", negsc_tweet)
def findTokensJson():
tokens = Counter()
for line in open(INPUT, 'r'):
for token in tokenize(json.loads(line)['text']):
tokens[token] += 1
output = open(OUTPUT, "wb")
tokens_pb = Tokens()
for token, count in tokens.most_common():
token_pb = tokens_pb.tokens.add()
token_pb.token = token
token_pb.count = count
output.write(tokens_pb.SerializeToString())
output.close
def findTokensJson():
tokens = Counter()
for line in open(INPUT, 'r'):
for token in tokenize(json.loads(line)['text']):
tokens[token] += 1
output = open(OUTPUT, "wb")
tokens_pb = Tokens()
for token, count in tokens.most_common():
token_pb = tokens_pb.tokens.add()
token_pb.token = token
token_pb.count = count
output.write(tokens_pb.SerializeToString())
output.close
def extractW2VFeaturesSim(w2vmodelfile, phrasemodel, tweets, targets, labels):
phmodel = Phrases.load(phrasemodel)
w2vmodel = word2vec.Word2Vec.load(w2vmodelfile)
inv_topics = {v: k for k, v in tokenize_tweets.TOPICS_LONG.items()}
for i, tweet in enumerate(tweets):
# get the neut/pos/neg hashtags
neut = KEYWORDS_NEUT[inv_topics[targets[i]]]
pos = KEYWORDS_POS[inv_topics[targets[i]]]
neg = KEYWORDS_NEG[inv_topics[targets[i]]]
tokenised_tweet = tokenize(tweet.lower())
words = filterStopwords(tokenised_tweet)
neutcnt, poscnt, negcnt = 0, 0, 0
neutsc, possc, negsc = 0.0, 0.0, 0.0
# transform, as earlier, with the phrase model
for token in phmodel[words]:
try:
neutsim = w2vmodel.similarity(neut, token)
neutcnt += 1
neutsc += neutsim
except KeyError:
neutsim = 0
try:
possim = w2vmodel.similarity(pos, token)
possc += possim
poscnt += 1
except KeyError:
possim = 0
try:
negsim = w2vmodel.similarity(neg, token)
negsc += negsim
negcnt += 1
except KeyError:
negsim = 0
#print targets[i], "\t", token, "\t", neutsim, "\t", possim, "\t", negsim
neutsc_tweet = neutsc / neutcnt
possc_tweet = possc / poscnt
negsc_tweet = negsc / negcnt
print(targets[i], "\t", labels[i], "\t", neutsc_tweet, "\t",
possc_tweet, "\t", negsc_tweet)
def findTokensOfficial():
tokens = Counter()
for line in io.open(INPUT, encoding='windows-1252', mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
for token in tokenize(line.split("\t")[2]): #For Trump it's [1]
tokens[token] += 1
output = open(OUTPUT, "wb")
tokens_pb = Tokens()
for token, count in tokens.most_common():
token_pb = tokens_pb.tokens.add()
token_pb.token = token
token_pb.count = count
output.write(tokens_pb.SerializeToString())
output.close
def extractFeaturesCrossTweetTarget(tweets, targets):
ret = []
inv_topics = {v: k for k, v in tokenize_tweets.TOPICS_LONG.items()}
#TOPICS = inv_topics.keys()
for i, tweet in enumerate(tweets):
tokenised_tweet = tokenize(tweet)
target_keywords = tokenize_tweets.KEYWORDS.get(inv_topics.get(targets[i]))
target_in_tweet = 0
for key in target_keywords:
if tweet.__contains__(key):
target_in_tweet = 1
break
# option below cares for tokenisation, but since hashtags are not tokenised at the moment, the above works better
#for tweettok in tokenised_tweet:
# if tweettok in target_keywords:
# target_in_tweet = 1
# break
ret.append(target_in_tweet)
return ret
def findTokensOfficial():
tokens = Counter()
for line in io.open(INPUT, encoding='windows-1252',
mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
for token in tokenize(line.split("\t")[2]): #For Trump it's [1]
tokens[token] += 1
output = open(OUTPUT, "wb")
tokens_pb = Tokens()
for token, count in tokens.most_common():
token_pb = tokens_pb.tokens.add()
token_pb.token = token
token_pb.count = count
output.write(tokens_pb.SerializeToString())
output.close
def extractFeaturesCrossTweetTarget(tweets, targets):
ret = []
inv_topics = {v: k for k, v in tokenize_tweets.TOPICS_LONG.items()}
#TOPICS = inv_topics.keys()
for i, tweet in enumerate(tweets):
tokenised_tweet = tokenize(tweet)
target_keywords = tokenize_tweets.KEYWORDS.get(
inv_topics.get(targets[i]))
target_in_tweet = 0
for key in target_keywords:
if tweet.__contains__(key):
target_in_tweet = 1
break
# option below cares for tokenisation, but since hashtags are not tokenised at the moment, the above works better
#for tweettok in tokenised_tweet:
# if tweettok in target_keywords:
# target_in_tweet = 1
# break
ret.append(target_in_tweet)
return ret
def thinposts(lines):
posts = []
for line in lines:
# if not re.search(subreddit_re, line):
# continue
comment = json.loads(line)
if comment['text'] == '[deleted]':
continue
if comment['community'][1]['name'].lower() in subreddits:
out_comment = {}
tokens = twokenize_wrapper.tokenize(comment['text'])
out_comment['body'] = comment['text']
out_comment['subreddit'] = comment['community'][1]['name']
out_comment['author'] = comment['user']['username']
out_comment['created_utc'] = str(int(comment['createdAt']) / 1000)
out_comment['tokens'] = tokens
out_comment['id'] = 'x'
yield(json.dumps(out_comment))
def main(argv):
import nltk
import random, re
import twokenize_wrapper as tok
import pickle
stopWords = {}
st = open('stopWordsNew.txt', 'r')
inputFile = open(argv[0],'r')
outputFile = open(argv[1],'w')
maxEntObjectFile = open('maxEntObject.pkl','rb')
for line in st:
line = line.strip('\n')
if(not stopWords.has_key(line)):
stopWords[line] = 1
def featureFunc(tweet):
feat = {}
for word in tweet:
feat[word] = 1
return feat
wnl = nltk.stem.WordNetLemmatizer()
wordListMap = {}
tokenizedTweets = []
totalTweets = 0
for line in inputFile:
tweet = line.strip('\n')
tweet = tweet.lower()
tweet = re.sub(r'#([^\s]+)', r'\1', tweet) #HASH TAG
tweet = re.sub(r'(@[\w]+)','_HANDLE_',tweet) #HANDLE
tweet = re.sub(r'http[\w://.~?=%&-]+','_URL_',tweet) #URL
tweet = re.sub(r'(/|:|&|\(|\))',' ',tweet) # / : & ( ) spaced
tweet = re.sub(r'(\d+)',r' \1 ',tweet) # Digit clusters spaced
tweet = re.sub(r'(\w+)(-|;)(\w+)',r'\1 \3',tweet) # words(-|;)word separated
tokens = tok.tokenize(tweet)
pattern = re.compile(r"(.)\1{2,}", re.DOTALL) # hunggggryy -> hungryy
newTokens = []
flag = 0
for word in tokens:
word = pattern.sub(r"\1", word)
# word = word.strip('\'"?,.!')
word = word.strip('.,();-*~[]_=|+%')
word = re.sub(r'(\w+)[..|.](\w+)',r'\1 \2',word)
newWord = word.split()
for word in newWord:
word = wnl.lemmatize(word)
# if(stopWords.has_key(word) or word == ''or word.isdigit())):
if(stopWords.has_key(word) or word == '' or word.isdigit() or word=='\''):
continue
else:
if(flag == 1):
word = "NOT_" + word
flag = 0
if(word == "n't" and flag == 0):
flag = 1
word = "not"
newTokens.append(word)
for word in newTokens:
# str = str + word + ' '
if(not wordListMap.has_key(word)):
wordListMap[word] = 1
if(len(newTokens)>0):
totalTweets = totalTweets + 1
tokenizedTweets.append(newTokens)
classifier = pickle.load(maxEntObjectFile)
for i in range(0,len(tokenizedTweets)):
testTweet = tokenizedTweets[i]
pred = classifier.classify(featureFunc(testTweet))
outputFile.write(str(pred)+"\n")
return
i += 1
dictMap = {}
i = 1
for line in open('%s/hbc/data/dictionaries' % (BASE_DIR)):
dictionary = line.rstrip('\n')
dictMap[i] = dictionary
i += 1
dict2label = {}
for line in open('%s/hbc/data/dict-label3' % (BASE_DIR)):
(dictionary, label) = line.rstrip('\n').split(' ')
dict2label[dictionary] = label
nLines = 1
for line in sys.stdin:
line = line.rstrip('\n')
words = twokenize_wrapper.tokenize(line)
seq_features = []
tags = []
goodCap = capClassifier.Classify(words) > 0.9
# POS Tagging the tweet
if posTagger:
pos = posTagger.TagSentence(words)
pos = [p.split(':')[0] for p in pos] # remove weights
else:
pos = fields[-1].split(' ')
# Chunking the tweet
if chunkTagger:
word_pos = zip(words, [p.split(':')[0] for p in pos])
def findTokensPhrases(phrasemodel="phrase.model", useDev=False):
tokencnt = Counter()
bigram = Phrases(phrasemodel)
twcntr = 0
supercntr = 0
trumpcntr = 0
for line in open(INPUT, 'r'):
twcntr += 1
tokenised = tokenize(json.loads(line)['text'].lower())
tokens = filterStopwords(tokenised) # filter stopwords
for token in bigram[
tokens]: # calling the phrase model, this leaves some as single tokens and feq occurring ones as bigrams
tokencnt[token] += 1
for line in io.open(tokenize_tweets.FILETRAIN,
encoding='windows-1252',
mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
tokens = filterStopwords(tokenize(
line.split("\t")[2].lower())) #For Trump it's [1]
for token in bigram[tokens]:
supercntr += 1
tokencnt[token] += 1
if useDev == True:
for line in io.open(tokenize_tweets.FILEDEV,
encoding='windows-1252',
mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
tokens = filterStopwords(tokenize(
line.split("\t")[2].lower())) #For Trump it's [1]
for token in bigram[tokens]:
supercntr += 1
tokencnt[token] += 1
for line in io.open(tokenize_tweets.FILETRUMP, encoding='utf-8',
mode='r'): #for the Trump file it's utf-8
if line.startswith('ID\t'):
continue
tokens = filterStopwords(tokenize(
line.split("\t")[1].lower())) #For Trump it's [1]
for token in bigram[tokens]:
trumpcntr += 1
tokencnt[token] += 1
output = open(OUTPUT, "wb")
tokens_pb = Tokens()
for token, count in tokencnt.most_common():
if count > 1: # not even worth saving singletons
token_pb = tokens_pb.tokens.add()
token_pb.token = token
token_pb.count = count
print "Saving token counts for ", tokencnt.__sizeof__(
), ". ", twcntr, " unlabelled tweets, ", trumpcntr, " Donald Trump tweets, ", supercntr, " labelled tweets"
output.write(tokens_pb.SerializeToString())
output.close
def process_text(self, sentence):
sentence = sentence.strip()
sentence = re.sub(self.open_a, '', sentence)
sentence = re.sub(self.close_a, '', sentence)
return tokenize(sentence.lower())
def convertTweetsToVec(topic="all", numtoks='all', phrasemodel=False, phrasemodelpath="phrase.model"):
print("Reading tokens")
tokens,tweets_on_topic,tweets = readToks(phrasemodel)
if phrasemodel==True:
bigram = Phrases(phrasemodelpath)
if numtoks != "all":
tokens_sub = tokens[:numtoks]
else:
tokens_sub = tokens
numtoks = tokens.__sizeof__()
tokenized_tweets = Tweets()
vects = []
norm_tweets = []
print("Converting JSON tweets")
if topic=='all':
#for topic in TOPICS:
for tweet in tweets:
vect = np.zeros(numtoks, dtype=bool) # dimensionality. the most frequent tokens have a low index, then we can do a cutoff. original: 93988
norm_tweet = []
tokenized = tokenized_tweets.tweets.add()
tokenized.tweet = tweet['text']
if phrasemodel == False:
tokenised_tweet = tokenize(tweet['text'])
else:
tokens = filterStopwords(tokenize(tweet['text'].lower()))
tokenised_tweet = bigram[tokens]
for token in tokenised_tweet:
try:
index = tokens_sub.index(token)
except ValueError:
index = -1
if index > -1:
vect[index] = 1
norm_tweet.append(token)
else:
norm_tweet.append('NULL')
#print(norm_tweet)
norm_tweets.append(norm_tweet)
vects.append(vect)
else: # discouraged, needs to be updated
for index in tweets_on_topic[topic]:
tweet = tweets[index]
vect = np.zeros(numtoks) # dimensionality. the most frequent tokens have a low index, then we can do a cutoff. original: 93988
norm_tweet = []
tokenized = tokenized_tweets.tweets.add()
tokenized.tweet = tweet['text']
for token in tokenize(tweet['text']):
try:
index = tokens_sub.index(token)
except ValueError:
index = -1
if index > -1:
vect[index] = 1
norm_tweet.append(token)
else:
norm_tweet.append('NULL')
print(norm_tweet)
norm_tweets.append(norm_tweet)
vects.append(vect)
print("Finished converting JSON tweets")
return tokens_sub,vects,norm_tweets
def extractFeatureVocab(tweets,
keyword="all",
usephrasemodel=True,
phrasemodel="phrase.model",
anon_targets=False):
tokencounts = Counter()
features_final = []
bigram = Phrases(phrasemodel)
#tokens_topic = []
#if keyword == "all":
# for top in tokenize_tweets.TOPICS:
# if top != 'clinton':
# for tok in tokenize(tokenize_tweets.TOPICS_LONG[top]):
# tokens_topic.append(tok)
#else:
# tokens_topic = tokenize(tokenize_tweets.TOPICS_LONG[keyword])
for tweet in tweets:
if usephrasemodel == False:
tokenised_tweet = tokenize(tweet)
for token in tokenised_tweet: #unigram features
tokencounts[token] += 1
#for toktopic in tokens_topic:
# tokencounts[toktopic + '|' + token] += 1
for l in zip(*[tokenised_tweet[i:]
for i in range(2)]): #bigram features
tokencounts["_".join(l)] += 1
#for ltop in zip(*[tokens_topic[i:] for i in range(2)]):
# tokencounts["_".join(ltop) + '|' + "_".join(l)] += 1
else:
# this includes unigrams and frequent bigrams
tokens = filterStopwords(tokenize(
tweet.lower())) #For Trump it's [1]
phrasetoks = bigram[tokens]
target_keywords = []
if anon_targets == True:
for top in tokenize_tweets.TOPICS:
if top == "climate": # hack, this is the only non-list value
target_keywords.append("climate")
else:
#for keyw in tokenize_tweets.KEYWORDS[top]:
target_keywords.extend(tokenize_tweets.KEYWORDS[top])
phrasetoks_new = []
for token in phrasetoks:
for keyw in target_keywords:
if keyw in token:
token = token.replace(keyw, "TARGET")
phrasetoks_new.append(token)
phrasetoks = phrasetoks_new
for token in phrasetoks:
tokencounts[token] += 1
for l in zip(*[phrasetoks[i:] for i in range(2)]):
tokencounts["_".join(l)] += 1
for token, count in tokencounts.most_common():
if count > 1:
features_final.append(token)
#print token, count
return features_final
def convertTweetsToVec(topic="all",
numtoks='all',
phrasemodel=False,
phrasemodelpath="phrase.model"):
print("Reading tokens")
tokens, tweets_on_topic, tweets = readToks(phrasemodel)
if phrasemodel == True:
bigram = Phrases(phrasemodelpath)
if numtoks != "all":
tokens_sub = tokens[:numtoks]
else:
tokens_sub = tokens
numtoks = tokens.__sizeof__()
tokenized_tweets = Tweets()
vects = []
norm_tweets = []
print("Converting JSON tweets")
if topic == 'all':
#for topic in TOPICS:
for tweet in tweets:
vect = np.zeros(
numtoks, dtype=bool
) # dimensionality. the most frequent tokens have a low index, then we can do a cutoff. original: 93988
norm_tweet = []
tokenized = tokenized_tweets.tweets.add()
tokenized.tweet = tweet['text']
if phrasemodel == False:
tokenised_tweet = tokenize(tweet['text'])
else:
tokens = filterStopwords(tokenize(tweet['text'].lower()))
tokenised_tweet = bigram[tokens]
for token in tokenised_tweet:
try:
index = tokens_sub.index(token)
except ValueError:
index = -1
if index > -1:
vect[index] = 1
norm_tweet.append(token)
else:
norm_tweet.append('NULL')
#print(norm_tweet)
norm_tweets.append(norm_tweet)
vects.append(vect)
else: # discouraged, needs to be updated
for index in tweets_on_topic[topic]:
tweet = tweets[index]
vect = np.zeros(
numtoks
) # dimensionality. the most frequent tokens have a low index, then we can do a cutoff. original: 93988
norm_tweet = []
tokenized = tokenized_tweets.tweets.add()
tokenized.tweet = tweet['text']
for token in tokenize(tweet['text']):
try:
index = tokens_sub.index(token)
except ValueError:
index = -1
if index > -1:
vect[index] = 1
norm_tweet.append(token)
else:
norm_tweet.append('NULL')
print(norm_tweet)
norm_tweets.append(norm_tweet)
vects.append(vect)
print("Finished converting JSON tweets")
return tokens_sub, vects, norm_tweets
feat_list = []
for word in words:
feat_list.append(self.fe.get_features(word))
# Add context features
feat_list = features.add_context_features(feat_list)
self.fe.add_bigram_features(feat_list)
# Create string to feed into Mallet
feat_list_str = []
for word_feats in feat_list:
feat_list_str.append(' '.join(word_feats))
self.tagger.stdin.write(
("\t".join(feat_list_str) + "\n").encode('utf8'))
pos = []
for i in range(len(feat_list)):
pos.append(self.tagger.stdout.readline().rstrip('\n').strip(' '))
self.nTagged += 1
return pos
if __name__ == "__main__":
posTagger = PosTagger()
for line in sys.stdin:
words = twokenize_wrapper.tokenize(line.strip())
if not words:
continue
pos = posTagger.TagSentence(words)
print "%s\t%s\t%s" % (line, " ".join(words), " ".join(pos))
def extractW2VHashFeatures(w2vmodel, phrasemodel, mode, tweets, targets,
labels):
features = []
inv_topics = {v: k for k, v in tokenize_tweets.TOPICS_LONG.items()}
for i, tweet in enumerate(tweets):
# get the neut/pos/neg hashtags
neut = KEYWORDS_NEUT[inv_topics[targets[i]]]
pos = KEYWORDS_POS[inv_topics[targets[i]]]
neg = KEYWORDS_NEG[inv_topics[targets[i]]]
neutsim = w2vmodel.most_similar(neut, topn=60)
possim = w2vmodel.most_similar(pos, topn=60)
negsim = w2vmodel.most_similar(neg, topn=60)
tokenised_tweet = tokenize(tweet.lower())
words = filterStopwords(tokenised_tweet)
neutcnt, poscnt, negcnt, neutsimp, possimp, negsimp = 0, 0, 0, 0, 0, 0
# transform, as earlier, with the phrase model
for token in phrasemodel[words]:
if neut == token:
neutsimp = 1
if pos == token:
possimp = 1
if neg == token:
negsimp = 1
for n, sc in neutsim:
if sc >= 0.4 and n == token:
neutcnt += 1
for n, sc in possim:
if sc >= 0.4 and n == token:
poscnt += 1
for n, sc in negsim:
if sc >= 0.4 and n == token:
negcnt += 1
#print targets[i], "\t", labels[i], "\t", neutcnt, "\t", poscnt, "\t", negcnt, "\t", neutsimp, "\t", possimp, "\t", negsimp
#featint = [neutcnt, poscnt, negcnt, neutsimp, possimp, negsimp]
pn = 0
if possim and negsim:
pn = 1
possimp = 0
negsimp = 0
if mode == "hash":
featint = [neutsimp, possimp, negsimp, pn]
features.append(featint)
if mode == "w2v_hash":
featint = [neutcnt, poscnt, negcnt, neutsimp, possimp, negsimp, pn]
features.append(featint)
featlabels = []
if mode == "hash":
featlabels = ["neut_hash", "pos_hash", "neg_hash", "posneg_hash"]
if mode == "w2v_hash":
featlabels = [
"neut_extw2v", "pos_extw2v", "neg_extw2v", "neut_hash", "pos_hash",
"neg_hash", "posneg_hash"
]
return features, featlabels
self.GetTagger()
feat_list = []
for word in words:
feat_list.append(self.fe.get_features(word))
# Add context features
feat_list = features.add_context_features(feat_list)
self.fe.add_bigram_features(feat_list)
# Create string to feed into Mallet
feat_list_str = []
for word_feats in feat_list:
feat_list_str.append(' '.join(word_feats))
self.tagger.stdin.write(("\t".join(feat_list_str) + "\n").encode('utf8'))
pos = []
for i in range(len(feat_list)):
pos.append(self.tagger.stdout.readline().rstrip('\n').strip(' '))
self.nTagged += 1
return pos
if __name__ == "__main__":
posTagger = PosTagger()
for line in sys.stdin:
words = twokenize_wrapper.tokenize(line.strip())
if not words:
continue
pos = posTagger.TagSentence(words)
print "%s\t%s\t%s" % (line, " ".join(words), " ".join(pos))
def create_project(sent_file, template_folder, new_proj_folder, new_proj_name):
# Check that directories/files exist (or don't)
if not os.path.isdir(template_folder):
print 'Template folder DNE: ', template_folder
return
elif os.path.isdir(new_proj_folder):
print 'Will overwrite the following directory: ', new_proj_folder
return
elif not os.path.exists(sent_file):
print 'Sentence files does not exist: ', sent_file
return
# Make sure the project name doesn't have spaces
new_proj_name = re.sub(' ', '_', new_proj_name)
# Copy over all standard mmax2 files
shutil.copytree(template_folder, new_proj_folder)
f = open(os.path.join(new_proj_folder, new_proj_name + '.mmax'), 'w')
f.write(MMAX_STRING % new_proj_name)
f.close()
# Creating word, pos, and sentence files
f_word = open(os.path.join(new_proj_folder,
new_proj_name + '_words.xml'), 'w')
f_pos = open(os.path.join(new_proj_folder,
new_proj_name + '_POS_level.xml'), 'w')
f_sent = open(os.path.join(new_proj_folder,
new_proj_name + '_sentence_level.xml'), 'w')
# Add headers
f_word.write(WORDS_HEADER_STRING)
f_pos.write(POS_HEADER_STRING)
f_sent.write(SENT_HEADER_STRING)
# For each sentence(tweet) in the file.
tweet_tokens_list = []
for tweet in open(sent_file):
tweet_tokens_list.append(twokenize_wrapper.tokenize(tweet.strip()))
# Load POS tagger and tag the tweets
pos_tagger = mallet_wrapper.MalletPOSTagger(_model_location, _token2pos,
_token, _temp_dir)
pos_tagged_tweets = pos_tagger.pos_tag_tweets(tweet_tokens_list)
# Add markables
word_count = 1
tweet_count = 1
for tagged_tweet in pos_tagged_tweets:
start_count = word_count
# For each word/pos in the sentence
for word, pos in tagged_tweet:
# Check if the word is a user, RT, or hash tag
new_pos = symbol_tag.tag_token(word)
if new_pos:
pos = new_pos
f_word.write(WORD_STRING % (word_count, word))
f_pos.write(POS_STRING % (word_count, word_count, pos.lower()))
word_count += 1
f_sent.write(SENT_STRING % (tweet_count, start_count, word_count - 1))
tweet_count += 1
# Add closing tags
f_word.write('</words>')
f_pos.write('</markables>')
f_sent.write('</markables>')
f_word.close()
f_pos.close()
f_sent.close()
tweets_on_topic = defaultdict(list)
for topic in topics:
for index, tweet in enumerate(tweets):
for keyword in keywords[topic]:
if keyword in tweet['text'].lower():
tweets_on_topic[topic].append(index)
break
for topic in topics:
tokenized_tweets = Tweets()
for index in tweets_on_topic[topic]:
tweet = tweets[index]
tokenized = tokenized_tweets.tweets.add()
tokenized.tweet = tweet['text']
for token in tokenize(tweet['text']):
try:
index = tokens.index(token)
tokenized.tokens.append(index)
except ValueError:
tokenized.tokens.append(-1)
f = open(topic + '.tweets', "wb")
f.write(tokenized_tweets.SerializeToString())
f.close()
def extractW2VHashFeatures(w2vmodel, phrasemodel, mode, tweets, targets, labels):
features = []
inv_topics = {v: k for k, v in tokenize_tweets.TOPICS_LONG.items()}
for i, tweet in enumerate(tweets):
# get the neut/pos/neg hashtags
neut = KEYWORDS_NEUT[inv_topics[targets[i]]]
pos = KEYWORDS_POS[inv_topics[targets[i]]]
neg = KEYWORDS_NEG[inv_topics[targets[i]]]
neutsim = w2vmodel.most_similar(neut, topn=60)
possim = w2vmodel.most_similar(pos, topn=60)
negsim = w2vmodel.most_similar(neg, topn=60)
tokenised_tweet = tokenize(tweet.lower())
words = filterStopwords(tokenised_tweet)
neutcnt, poscnt, negcnt, neutsimp, possimp, negsimp = 0, 0, 0, 0, 0, 0
# transform, as earlier, with the phrase model
for token in phrasemodel[words]:
if neut == token:
neutsimp = 1
if pos == token:
possimp = 1
if neg == token:
negsimp = 1
for n, sc in neutsim:
if sc >= 0.4 and n == token:
neutcnt += 1
for n, sc in possim:
if sc >= 0.4 and n == token:
poscnt += 1
for n, sc in negsim:
if sc >= 0.4 and n == token:
negcnt += 1
#print targets[i], "\t", labels[i], "\t", neutcnt, "\t", poscnt, "\t", negcnt, "\t", neutsimp, "\t", possimp, "\t", negsimp
#featint = [neutcnt, poscnt, negcnt, neutsimp, possimp, negsimp]
pn = 0
if possim and negsim:
pn = 1
possimp = 0
negsimp = 0
if mode == "hash":
featint = [neutsimp, possimp, negsimp, pn]
features.append(featint)
if mode == "w2v_hash":
featint = [neutcnt, poscnt, negcnt, neutsimp, possimp, negsimp, pn]
features.append(featint)
featlabels = []
if mode == "hash":
featlabels = ["neut_hash", "pos_hash", "neg_hash", "posneg_hash"]
if mode == "w2v_hash":
featlabels = ["neut_extw2v", "pos_extw2v", "neg_extw2v", "neut_hash", "pos_hash", "neg_hash", "posneg_hash"]
return features, featlabels
tweets_on_topic = defaultdict(list)
for topic in topics:
for index, tweet in enumerate(tweets):
for keyword in keywords[topic]:
if keyword in tweet['text'].lower():
tweets_on_topic[topic].append(index)
break
for topic in topics:
tokenized_tweets = Tweets()
for index in tweets_on_topic[topic]:
tweet = tweets[index]
tokenized = tokenized_tweets.tweets.add()
tokenized.tweet = tweet['text']
for token in tokenize(tweet['text']):
try:
index = tokens.index(token)
tokenized.tokens.append(index)
except ValueError:
tokenized.tokens.append(-1)
f = open(topic + '.tweets', "wb")
f.write(tokenized_tweets.SerializeToString())
f.close()
i += 1
dictMap = {}
i = 1
for line in open('%s/hbc/data/dictionaries' % (BASE_DIR)):
dictionary = line.rstrip('\n')
dictMap[i] = dictionary
i += 1
dict2label = {}
for line in open('%s/hbc/data/dict-label3' % (BASE_DIR)):
(dictionary, label) = line.rstrip('\n').split(' ')
dict2label[dictionary] = label
nLines = 1
for line in sys.stdin:
line = line.rstrip('\n')
words = twokenize_wrapper.tokenize(line)
seq_features = []
tags = []
goodCap = capClassifier.Classify(words) > 0.9
# POS Tagging the tweet
if posTagger:
pos = posTagger.TagSentence(words)
pos = [p.split(':')[0] for p in pos] # remove weights
else:
pos = fields[-1].split(' ')
# Chunking the tweet
if chunkTagger:
word_pos = zip(words, [p.split(':')[0] for p in pos])
