def extract_features(tweet):
word_features = get_word_features(tweet)
tweet_words = set(tweet)
features = {}
for word in word_features:
features['contains(%s)' % word] = (word in tweet_words)
return features
''' Train on file "trainGold.tsv"
Test on file "devGold.tsv"
(attempted to view confusion matrix. bugs...)
'''
# TRAIN
read = readerAndWriter.readFile("data/cleaned/trainGold.tsv")
dataForTweets = readTweets(read)
training_set = nltk.classify.apply_features(extract_features, dataForTweets)
classifier = nltk.NaiveBayesClassifier.train(training_set)
# TEST
testFile = readerAndWriter.readFile("data/cleaned/devGold.tsv")
dataForTest = readTweets(testFile)
test_set = nltk.classify.apply_features(extract_features, dataForTest)
print "CLASSIFYING: ", nltk.classify.accuracy(classifier, test_set)
classifier.show_most_informative_features(10)
'''
cm = nltk.ConfusionMatrix(training_set, training_set)
print(cm.pretty_format(sort_by_count=True, show_percents=True, truncate=9))
'''
#result = []
#for tweet in tagResults: #w = word, t = tag, c = confidence level
# cleanTweet = ""
# for triple in tweet:
# print triple
# (w, t, c) = triple
# #removing urls, user mentions, numbers, and hashtags from the tweet
# if t != 'U' and t != '@' and t!= '$' and t != '#':
# cleanTweet+=str(w) + " "
# #print "cleantweet is:",cleanTweet
# #print w
# print cleanTweet
# result.append(cleanTweet)
#print result
#readResults = readerAndWriter.readFile("cleaned/PFTweets.txt")
readTrain = readerAndWriter.readFile("cleaned/PFTweetsTrain.txt")
readTest = readerAndWriter.readFile("cleaned/PFTweetsTest.txt")
#print "read results\n",readResults
print "\n DONE READING \n"
#print "\nclean tweet\n",cleanTweet(tweets)
#replacedResults = replaceTaggedTweet(readResults)
taggedTrain = replaceTaggedTweet(readTrain)
taggedTest = replaceTaggedTweet(readTest)
print "\n DONE REPLACING \n"
#print "\nreplace tagged tweet\t",replacedResults
#np.savetxt("cleaned/testCleaned.txt",replacedResults)
#toFile("cleaned/PFTweetsCleaned.txt",replacedResults)
toFile("cleaned/PFTrainTag.txt", taggedTrain)
toFile("cleaned/PFTestTag.txt", taggedTest)
print "\n DONE WRITING \n"
'''
def collapseScales(originalData):
# newFile is an array of arrays where each element in newFile
# is a line (as an array)
newFile = []
for e in originalData:
line = e.split("\t")
if (int(line[2]) == -2):
line[2] = str(-1)
elif (int(line[2]) == 2):
line[2] = str(1)
newFile.append(line)
return newFile
''' ******************* COLLAPSE SCALES ***************************'''
originalFile = readerAndWriter.readFile("cleaned/allTopics.tsv")
# find the distribution of the labels before collapsing the scales
dist_count = [0]*5
for element in originalFile:
line = element.split("\t")
dist_count[int(line[2])+2] += 1
print "Distribution count of numbers [-2, -1, 0, 1, 2]:",dist_count #[139, 1086, 2667, 4608, 549]
print "Total number of tweets",np.sum(dist_count) #9049
distribution = [0]*5
for num in range(len(dist_count)):
print "num is", num
print dist_count[num]
distribution[num] = dist_count[num]/np.sum(dist_count)
#result = []
#for tweet in tagResults: #w = word, t = tag, c = confidence level
# cleanTweet = ""
# for triple in tweet:
# print triple
# (w, t, c) = triple
# #removing urls, user mentions, numbers, and hashtags from the tweet
# if t != 'U' and t != '@' and t!= '$' and t != '#':
# cleanTweet+=str(w) + " "
# #print "cleantweet is:",cleanTweet
# #print w
# print cleanTweet
# result.append(cleanTweet)
#print result
#readResults = readerAndWriter.readFile("cleaned/PFTweets.txt")
readTrain = readerAndWriter.readFile("cleaned/PFTweetsTrain.txt")
readTest = readerAndWriter.readFile("cleaned/PFTweetsTest.txt")
#print "read results\n",readResults
print "\n DONE READING \n"
#print "\nclean tweet\n",cleanTweet(tweets)
#replacedResults = replaceTaggedTweet(readResults)
taggedTrain = replaceTaggedTweet(readTrain)
taggedTest = replaceTaggedTweet(readTest)
print "\n DONE REPLACING \n"
#print "\nreplace tagged tweet\t",replacedResults
#np.savetxt("cleaned/testCleaned.txt",replacedResults)
#toFile("cleaned/PFTweetsCleaned.txt",replacedResults)
toFile("cleaned/PFTrainTag.txt", taggedTrain)
toFile("cleaned/PFTestTag.txt", taggedTest)
print "\n DONE WRITING \n"
def collapseScales(originalData):
# newFile is an array of arrays where each element in newFile
# is a line (as an array)
newFile = []
for e in originalData:
line = e.split("\t")
if (int(line[2]) == -2):
line[2] = str(-1)
elif (int(line[2]) == 2):
line[2] = str(1)
newFile.append(line)
return newFile
''' ******************* COLLAPSE SCALES ***************************'''
originalFile = readerAndWriter.readFile("cleaned/allTopics.tsv")
# find the distribution of the labels before collapsing the scales
dist_count = [0] * 5
for element in originalFile:
line = element.split("\t")
dist_count[int(line[2]) + 2] += 1
print "Distribution count of numbers [-2, -1, 0, 1, 2]:", dist_count #[139, 1086, 2667, 4608, 549]
print "Total number of tweets", np.sum(dist_count) #9049
distribution = [0] * 5
for num in range(len(dist_count)):
print "num is", num
print dist_count[num]
distribution[num] = dist_count[num] / np.sum(dist_count)
