def buildWD(file_name, writeCSV=False):
print "Building word dictionary"
wordRowDict, numTweets, rownames = buildWords(file_name)
print "Word dictionary finished"
mat = np.zeros((len(wordRowDict), numTweets))
colnames = []
subjects = []
print "Building word document matrix"
f = open(file_name)
matCol = 0
for line in f:
words = line.strip('\"').split(',')
tweet = buildTweet(words[5:])
tweetColumn = np.zeros(len(wordRowDict))
for word in tweetprocess.tokenize(tweet):
if word in wordRowDict:
tweetColumn[wordRowDict[word]] = tweetColumn[wordRowDict[word]] + 1
if np.sum(tweetColumn) > 0.5*(len(words)-2):
colnames.append(words[1])
subjects.append(words[0])
mat[:,matCol] = tweetColumn
matCol += 1
f.close()
mat = mat[:,0:matCol]
print "Word document matrix finished"
if writeCSV:
print "Writing to CSV"
writeToCSV(mat, colnames, wordRowDict, "trainWords.csv")
print "Finished writing to CSV"
return (mat, colnames, rownames, subjects)
def buildWords(file_name):
wordCountDict = defaultdict(int)
wordRowDict = {}
rownames = []
numTweets = 0
f = open(file_name)
if "_tiny" in file_name or "_micro" in file_name:
thresh = 10
else:
thresh = 50
row = 0
for line in f:
numTweets += 1
words = line.split()
tweet = buildTweet(words[2:])
for word in tweetprocess.tokenize(tweet):
wordCountDict[word] = wordCountDict[word] + 1
if wordCountDict[word] > thresh and word not in wordRowDict:
rownames.append(word)
wordRowDict[word] = row
row += 1
f.close()
i = 0
for word in wordRowDict:
if i == 15:
break
i += 1
print len(wordRowDict)
return wordRowDict, numTweets, rownames
def buildWords(file_name):
wordCountDict = defaultdict(int)
wordRowDict = {}
rownames = []
numTweets = 0
f = open(file_name)
if "_tiny" in file_name or "_micro" in file_name:
thresh = 10
else:
thresh = 50
row = 0
for line in f:
numTweets += 1
words = line.split()
tweet = buildTweet(words[2:])
for word in tweetprocess.tokenize(tweet):
wordCountDict[word] = wordCountDict[word] + 1
if wordCountDict[word] > thresh and word not in wordRowDict:
rownames.append(word)
wordRowDict[word] = row
row += 1
f.close()
i = 0
for word in wordRowDict:
if i == 15:
break
i += 1
print len(wordRowDict)
return wordRowDict, numTweets, rownames
def buildWords(file_name):
wordCountDict = defaultdict(int)
wordRowDict = {}
rownames = []
numTweets = 0
f = open(file_name)
thresh = 5
row = 0
for line in f:
numTweets += 1
words = line.strip('\"').split(',')
tweet = buildTweet(words[5:])
for word in tweetprocess.tokenize(tweet):
wordCountDict[word] = wordCountDict[word] + 1
if wordCountDict[word] > thresh and word not in wordRowDict:
rownames.append(word)
wordRowDict[word] = row
row += 1
f.close()
i = 0
for word in wordRowDict:
if i == 15:
break
i += 1
print "Word dict length: ", len(wordRowDict)
return wordRowDict, numTweets, rownames
def buildWD(file_name, writeCSV=False):
print "Building word dictionary"
wordRowDict, numTweets, rownames = buildWords(file_name)
print "Word dictionary finished"
mat = np.zeros((len(wordRowDict), numTweets))
colnames = []
subjects = []
print "Building word document matrix"
f = open(file_name)
matCol = 0
for line in f:
words = line.strip('\"').split(',')
tweet = buildTweet(words[5:])
tweetColumn = np.zeros(len(wordRowDict))
for word in tweetprocess.tokenize(tweet):
if word in wordRowDict:
tweetColumn[
wordRowDict[word]] = tweetColumn[wordRowDict[word]] + 1
if np.sum(tweetColumn) > 0.5 * (len(words) - 2):
colnames.append(words[1])
subjects.append(words[0])
mat[:, matCol] = tweetColumn
matCol += 1
f.close()
mat = mat[:, 0:matCol]
print "Word document matrix finished"
if writeCSV:
print "Writing to CSV"
writeToCSV(mat, colnames, wordRowDict, "trainWords.csv")
print "Finished writing to CSV"
return (mat, colnames, rownames, subjects)
def buildWords(file_name):
wordCountDict = defaultdict(int)
wordRowDict = {}
rownames = []
numTweets = 0
f = open(file_name)
thresh = 5
row = 0
for line in f:
numTweets += 1
words = line.strip('\"').split(',')
tweet = buildTweet(words[5:])
for word in tweetprocess.tokenize(tweet):
wordCountDict[word] = wordCountDict[word] + 1
if wordCountDict[word] > thresh and word not in wordRowDict:
rownames.append(word)
wordRowDict[word] = row
row += 1
f.close()
i = 0
for word in wordRowDict:
if i == 15:
break
i += 1
print "Word dict length: ", len(wordRowDict)
return wordRowDict, numTweets, rownames
def tfidf_logreg(train_file):
wd = sentiment_buildwd.buildWD(train_file)
colnames = wd[1]
rownames = wd[2]
subjects = wd[3]
idf = tfidf(wd[0], rownames)
trainMat = np.zeros((len(colnames), wd[0].shape[1]))
f = open(train_file)
matCol = 0
for line in f:
words = line.strip('\"').split(',')
if words[1] in colnames:
trainRow = np.zeros(wd[0].shape[1])
numWords = 0
tweet = sentiment_buildwd.buildTweet(words[5:])
for word in tweetprocess.tokenize(tweet):
pword = sentiment_buildwd.processWord(word)
if pword in rownames:
numWords += 1
trainRow = trainRow + idf[0][rownames.index(pword)]
trainRow = (trainRow*1.0) / numWords
trainMat[matCol,:] = trainRow
matCol += 1
f.close()
trainVals = sentiment_buildwd.trainValsFromSubjects(subjects)
# RANDOMIZE
random.seed(17)
shuffle = range(len(subjects))
random.shuffle(shuffle)
train = []
labels = []
index = 0
for i in shuffle:
train.append(trainMat[i])
labels.append(trainVals[i])
index += 1
cutoff = int(index*0.7)
logreg = linear_model.LogisticRegression()
logreg.fit(train[0:cutoff], labels[0:cutoff])
return logreg.score(train[cutoff:], labels[cutoff:])
def tfidf_logreg(train_file):
wd = sentiment_buildwd.buildWD(train_file)
colnames = wd[1]
rownames = wd[2]
subjects = wd[3]
idf = tfidf(wd[0], rownames)
trainMat = np.zeros((len(colnames), wd[0].shape[1]))
f = open(train_file)
matCol = 0
for line in f:
words = line.strip('\"').split(',')
if words[1] in colnames:
trainRow = np.zeros(wd[0].shape[1])
numWords = 0
tweet = sentiment_buildwd.buildTweet(words[5:])
for word in tweetprocess.tokenize(tweet):
pword = sentiment_buildwd.processWord(word)
if pword in rownames:
numWords += 1
trainRow = trainRow + idf[0][rownames.index(pword)]
trainRow = (trainRow * 1.0) / numWords
trainMat[matCol, :] = trainRow
matCol += 1
f.close()
trainVals = sentiment_buildwd.trainValsFromSubjects(subjects)
# RANDOMIZE
random.seed(17)
shuffle = range(len(subjects))
random.shuffle(shuffle)
train = []
labels = []
index = 0
for i in shuffle:
train.append(trainMat[i])
labels.append(trainVals[i])
index += 1
cutoff = int(index * 0.7)
logreg = linear_model.LogisticRegression()
logreg.fit(train[0:cutoff], labels[0:cutoff])
return logreg.score(train[cutoff:], labels[cutoff:])
def buildWD(file_name, writeCSV=False, randomize=False, sentiment=False):
print "Building word dictionary"
wordRowDict, numTweets, rownames = buildWords(file_name)
print "Word dictionary finished"
extra_feats = 0
if sentiment: extra_feats = 1
mat = np.zeros((len(wordRowDict) + extra_feats, numTweets))
colnames = []
subjects = []
print "Building word document matrix"
f = open(file_name)
matCol = 0
if (sentiment):
sentiment_model, sentiment_words = sent.tfidf_logreg(
SENTIMENT_FILENAME)
sentimentTweetColumn = np.zeros(len(sentiment_words))
for line in f:
words = line.split()
tweet = buildTweet(words[2:])
tweetColumn = np.zeros(len(wordRowDict) + extra_feats)
num_words = 0
for word in tweetprocess.tokenize(tweet):
if word in wordRowDict:
tweetColumn[
wordRowDict[word]] = tweetColumn[wordRowDict[word]] + 1
if sentiment and word in sentiment_words:
sentimentTweetColumn[sentiment_words.index(word)] += 1
num_words += 1
if (sentiment and num_words > 0):
sentimentTweetColumn *= 1.0 / num_words
tweetColumn[-1] = sentiment_model.predict(sentimentTweetColumn)
elif (sentiment):
tweetColumn[-1] = 2
if np.sum(tweetColumn) > 0.5 * (len(words) - 2):
colnames.append(words[0])
subjects.append(words[1])
mat[:, matCol] = tweetColumn
matCol += 1
f.close()
mat = mat[:, 0:matCol]
print "Word document matrix finished"
if writeCSV:
print "Writing to CSV"
writeToCSV(mat, colnames, wordRowDict, "trainWords.csv")
print "Finished writing to CSV"
if randomize:
random.seed(17)
# RANDOMIZE
shuffle = range(len(subjects))
random.shuffle(shuffle)
m = np.zeros(mat.shape)
c = []
s = []
index = 0
for i in shuffle:
m[:, index] = mat[:, i]
c.append(colnames[i])
s.append(subjects[i])
index += 1
return (m, c, rownames, s)
#return (mat, colnames, rownames, subjects)
return (mat, colnames, rownames, subjects)
def buildWD(file_name, writeCSV=False, randomize=False, sentiment=False):
print "Building word dictionary"
wordRowDict, numTweets, rownames = buildWords(file_name)
print "Word dictionary finished"
extra_feats = 0
if sentiment: extra_feats = 1
mat = np.zeros((len(wordRowDict) + extra_feats, numTweets))
colnames = []
subjects = []
print "Building word document matrix"
f = open(file_name)
matCol = 0
if (sentiment):
sentiment_model, sentiment_words = sent.tfidf_logreg(SENTIMENT_FILENAME)
sentimentTweetColumn = np.zeros(len(sentiment_words))
for line in f:
words = line.split()
tweet = buildTweet(words[2:])
tweetColumn = np.zeros(len(wordRowDict) + extra_feats)
num_words = 0
for word in tweetprocess.tokenize(tweet):
if word in wordRowDict:
tweetColumn[wordRowDict[word]] = tweetColumn[wordRowDict[word]] + 1
if sentiment and word in sentiment_words:
sentimentTweetColumn[sentiment_words.index(word)] += 1
num_words += 1
if (sentiment and num_words > 0):
sentimentTweetColumn *= 1.0 / num_words
tweetColumn[-1] = sentiment_model.predict(sentimentTweetColumn)
elif (sentiment):
tweetColumn[-1] = 2
if np.sum(tweetColumn) > 0.5*(len(words)-2):
colnames.append(words[0])
subjects.append(words[1])
mat[:,matCol] = tweetColumn
matCol += 1
f.close()
mat = mat[:,0:matCol]
print "Word document matrix finished"
if writeCSV:
print "Writing to CSV"
writeToCSV(mat, colnames, wordRowDict, "trainWords.csv")
print "Finished writing to CSV"
if randomize:
random.seed(17)
# RANDOMIZE
shuffle = range(len(subjects))
random.shuffle(shuffle)
m = np.zeros(mat.shape)
c = []
s = []
index = 0
for i in shuffle:
m[:, index] = mat[:, i]
c.append(colnames[i])
s.append(subjects[i])
index += 1
return (m, c, rownames, s)
#return (mat, colnames, rownames, subjects)
return (mat, colnames, rownames, subjects)