def getSentimentAnalyzer():
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_pos_words = get_all_words(positive_cleaned_tokens_list)
freq_dist_pos = FreqDist(all_pos_words)
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset
classifier = NaiveBayesClassifier.train(train_data)
return classifier
def startAnalysis():
# tokenize positive_tweets
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens))
pos_tweet_for_model = get_tweets_for_model(positive_cleaned_tokens_list)
pos_dataset = [(tweet_dict,"Positive") for tweet_dict in pos_tweet_for_model]
neg_tweet_for_model = get_tweets_for_model(negative_cleaned_tokens_list)
neg_dataset = [(tweet_dict,"Negative") for tweet_dict in neg_tweet_for_model]
dataset = pos_dataset + neg_dataset
random.shuffle(dataset)
train_data = dataset[:7000]
test_data= dataset[7000:]
classifier = NaiveBayesClassifier.train(train_data)
print("Accuracy is:", classify.accuracy(classifier, test_data))
print(classifier.show_most_informative_features(10))
def train(self):
positive_tweet_tokens = twitter_samples.tokenized(
"positive_tweets.json")
negative_tweet_tokens = twitter_samples.tokenized(
"negative_tweets.json")
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(
self.clear_data(tokens, self.stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(
self.clear_data(tokens, self.stop_words))
positive_tokens_for_model = self.get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = self.get_tweets_for_model(
negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
return NaiveBayesClassifier.train(dataset)
def fit(self, dataset=None):
"""
This Method will initialize the Training for model using data sample present in nltk libraries
:param dataset:
:return:model object
"""
positive_tweet_tokens = twitter_samples.tokenized(
'positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized(
'negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(cleanData(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(cleanData(tokens, stop_words))
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
self.classifier = nltk.NaiveBayesClassifier.train(dataset)
return self
def __init__(self):
print("normalizing twitter samples...")
self.stop_words = stopwords.words('english')
self.positive_tweet_tokens = twitter_samples.tokenized(
'positive_tweets.json')
self.negative_tweet_tokens = twitter_samples.tokenized(
'negative_tweets.json')
def getSentimentAnalyzer():
stop_words = stopwords.words('english')
posTokens = twitter_samples.tokenized('positive_tweets.json')
negTokens = twitter_samples.tokenized('negative_tweets.json')
posCleanedTokens = []
negCleanedTokens = []
for tokens in posTokens:
posCleanedTokens.append(removeNoise(tokens, stop_words))
for tokens in negTokens:
negCleanedTokens.append(removeNoise(tokens, stop_words))
allPosWords = get_all_words(posCleanedTokens)
freqDistPos = FreqDist(allPosWords)
print(freqDistPos.most_common(10))
posTokensModel = get_tweets_for_model(posCleanedTokens)
negTokensModel = get_tweets_for_model(negCleanedTokens)
posData = [(tweet_dict, "Positive") for tweet_dict in posTokensModel]
negData = [(tweet_dict, "Negative") for tweet_dict in negTokensModel]
dataset = posData + negData
random.shuffle(dataset)
train_data = dataset
classifier = NaiveBayesClassifier.train(train_data)
return classifier
def preprocess_data(self):
self.pos_tweets = twitter_samples.strings('positive_tweets.json')
self.neg_tweets = twitter_samples.strings('negative_tweets.json')
self.text = twitter_samples.strings('tweets.20150430-223406.json')
self.pos_tweet_tokens = twitter_samples.tokenized(
'positive_tweets.json')
self.neg_tweet_tokens = twitter_samples.tokenized(
'negative_tweets.json')
self.pos_cleaned_tokens_list = [
self.remove_noise(tokens) for tokens in self.pos_tweet_tokens
]
self.neg_cleaned_tokens_list = [
self.remove_noise(tokens) for tokens in self.neg_tweet_tokens
]
self.all_pos_words = self.get_all_words(self.pos_cleaned_tokens_list)
self.all_neg_words = self.get_all_words(self.neg_cleaned_tokens_list)
self.freq_dist_pos = FreqDist(self.all_pos_words)
self.freq_dist_neg = FreqDist(self.all_neg_words)
self.pos_tokens_for_model = self.get_tweets_for_model(
self.pos_cleaned_tokens_list)
self.neg_tokens_for_model = self.get_tweets_for_model(
self.neg_cleaned_tokens_list)
self.pos_dataset = [(tweet_dict, "Positive")
for tweet_dict in self.pos_tokens_for_model]
self.neg_dataset = [(tweet_dict, "Negative")
for tweet_dict in self.neg_tokens_for_model]
self.dataset = self.pos_dataset + self.neg_dataset
random.shuffle(self.dataset)
mid = len(self.dataset) // 2
self.train_data = self.dataset[:mid]
self.test_data = self.dataset[mid:]
def prepare_sentiment_classifier():
if(not os.path.exists('./.venv/nltk_data')):
os.makedirs('./.venv/nltk_data')
nltk.download('twitter_samples', './.venv/nltk_data/')
nltk.download('punkt', './.venv/nltk_data/')
nltk.download('averaged_perceptron_tagger','./.venv/nltk_data/')
nltk.download('wordnet','./.venv/nltk_data/')
nltk.download('stopwords','./.venv/nltk_data/')
#Normalization - canonical form conversion
#Define a lexical tags in the tweets and lemmatize (remove the past form, ending etc.)
#Then remove noise and stop words
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_pos_words = get_all_words(positive_cleaned_tokens_list)
# List the most used positive words
freq_dist_pos = FreqDist(all_pos_words)
print(freq_dist_pos.most_common(10))
# Prepare a dictionary for Bayes
positive_tokens_for_model = prepare_tweets_for_model(positive_cleaned_tokens_list)
negative_tokens_for_model = prepare_tweets_for_model(negative_cleaned_tokens_list)
negative_sentiment_value = -10
positive_sentiment_value = 10
positive_dataset = [(tweet_dict, positive_sentiment_value)
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, negative_sentiment_value)
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
#Mix the data to avoid bias
random.shuffle(dataset)
train_data = dataset[:7000]
#The rest 3k of 10k tweets are for testing
test_data = dataset[7000:]
#Train the model
classifier = NaiveBayesClassifier.train(train_data)
print("Accuracy is:", classify.accuracy(classifier, test_data))
#print(classifier.show_most_informative_features(10))
return classifier
def __init__(self):
""" This class trains the data on 10000 tweets """
self.stop_words = stopwords.words('english')
self.positive_cleaned_tokens_list = []
self.negative_cleaned_tokens_list = []
self.positive_tweets_tokens = twitter_samples.tokenized('positive_tweets.json')
self.negative_tweets_tokens = twitter_samples.tokenized('negative_tweets.json')
def __init__(self):
self.stop_words = stopwords.words('english')
self.positive_cleaned_tokens_list = []
self.negative_cleaned_tokens_list = []
self.positive_tweets_tokens = twitter_samples.tokenized(
'positive_tweets.json')
self.negative_tweets_tokens = twitter_samples.tokenized(
'negative_tweets.json')
def assign_cleans(self):
print('Training AI...')
print('Learning Positive Tweets')
for tokens in twitter_samples.tokenized('positive_tweets.json'):
self.clean_positive.append(remove_noise(tokens))
print('Learning Negative Tweets')
for tokens in twitter_samples.tokenized('negative_tweets.json'):
self.clean_negative.append(remove_noise(tokens))
def load_raw_training_data(self):
#Load the in built NLTK training data. Used other data sets if necessary
#to improve accuracy.
positive_data_tokenized = twitter_samples.tokenized(
'positive_tweets.json')
negative_data_tokenized = twitter_samples.tokenized(
'negative_tweets.json')
return positive_data_tokenized, negative_data_tokenized
def driver():
# String varaiables of the dataset
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
text = twitter_samples.strings('tweets.20150430-223406.json')
stop_words = stopwords.words('english')
# Tokenized variables of dataset
# tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
# Cleaning the noise in the tweet tokens
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
# Frequency distribution for cleaned words
all_pos_words = get_all_words(positive_cleaned_tokens_list)
freq_dist_pos = FreqDist(all_pos_words)
# Creating positive and negative dictionaries
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
# Creating the final dataset for training
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
# Shuffling for suedo-randomness and avoid bias
random.shuffle(dataset)
# Dividing shuffled data into train and test data
train_data = dataset[:7000]
test_data = dataset[7000:]
# Initializing the classifier
classifier = NaiveBayesClassifier.train(train_data)
custom_tweet = "I ordered just once from TerribleCo, they screwed up, never used the app again."
custom_tokens = remove_noise(word_tokenize(custom_tweet))
print(custom_tweet, classifier.classify(
dict([token, True] for token in custom_tokens)))
def train_model():
"""
Trains a Naive Bayes sentiment classifier using the twitter_samples
dataset from NLTK. Each tweet is tokenized and cleaned to produce a training
dataset for the machine learning model.
Parameters
----------
Returns
-------
NaiveBayesClassifier
"""
#Load dataset from nltk data
positive_tweets = twitter_samples.strings("positive_tweets.json")
negative_tweets = twitter_samples.strings("negative_tweets.json")
#Retrieve english stop words
stop_words = stopwords.words("english")
#Tweet tokenization
positive_tweet_tokens = twitter_samples.tokenized("positive_tweets.json")
negative_tweet_tokens = twitter_samples.tokenized("negative_tweets.json")
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
#Token cleaning
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
#Extract words from tokens
all_pos_words = get_all_words(positive_cleaned_tokens_list)
#Frequency distribition of words
freq_dist_pos = FreqDist(all_pos_words)
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
#Create datasets
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
#Merge individual datasets into singular training data
dataset = positive_dataset + negative_dataset
train_data = dataset
classifier = NaiveBayesClassifier.train(train_data)
return classifier
def main_code(analysis_input):
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
text = twitter_samples.strings('tweets.20150430-223406.json')
tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_pos_words = get_all_words(positive_cleaned_tokens_list)
freq_dist_pos = FreqDist(all_pos_words)
# print(freq_dist_pos.most_common(10))
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset[:7000]
test_data = dataset[7000:]
classifier = NaiveBayesClassifier.train(train_data)
# print("Accuracy is:", classify.accuracy(classifier, test_data))
# print(classifier.show_most_informative_features(10))
custom_tokens = remove_noise(word_tokenize(analysis_input))
pos_or_neg = str(
classifier.classify(dict([token, True] for token in custom_tokens)))
return pos_or_neg
def create_model():
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
# print(positive_tweet_tokens)
# print(positive_cleaned_tokens_list)
#FINDING WORD DISTRIBUTION
# all_pos_words = get_all_words(positive_cleaned_tokens_list)
# freq_dist_pos = FreqDist(all_pos_words)
# print(freq_dist_pos.most_common(10))
# MODEL PREPARATION
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
# code attaches a Positive or Negative label to each tweet
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
# print(positive_dataset)
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
# number of tweets is 10000 -- ratio of 70:30 for training and testing
train_data = dataset[:7000]
test_data = dataset[7000:]
# MODEL TRAINING
classifier = NaiveBayesClassifier.train(train_data)
# MODEL SAVING WITH PICKLE
filename = 'model/model_pickle1.sav'
pickle.dump(classifier, open(filename, 'wb'))
# MODEL SAVING WITH cPICKLE
filename = 'model/model_cpickle1.sav'
cPickle.dump(classifier, open(filename, 'wb'))
# MODEL SAVING WITH JOBLIB
filename = 'model/model_joblib1.sav'
joblib.dump(classifier, filename)
# MODEL ACCURACY
print("Accuracy is:", classify.accuracy(classifier, test_data))
print(classifier.show_most_informative_features(10))
def calibrate():
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
text = twitter_samples.strings('tweets.20150430-223406.json')
tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized(
'positive_tweets.json'
) #files downloaded from setup.py used to calibrate the classifer for sentiment analysis
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_pos_words = get_all_words(positive_cleaned_tokens_list)
freq_dist_pos = FreqDist(all_pos_words)
#print(freq_dist_pos.most_common(10))
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
positive_dataset = [
(tweet_dict, "Positive") #calibrating positive
for tweet_dict in positive_tokens_for_model
]
negative_dataset = [
(tweet_dict, "Negative") #calibrating negative
for tweet_dict in negative_tokens_for_model
]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset[:7000]
test_data = dataset[7000:]
global classifier
classifier = NaiveBayesClassifier.train(train_data) #trains the data!
print("Calibration complete!")
print("Accuracy is:", classify.accuracy(classifier, test_data))
def train_social():
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
text = twitter_samples.strings('tweets.20150430-223406.json')
# tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_pos_words = get_all_words(positive_cleaned_tokens_list)
freq_dist_pos = FreqDist(all_pos_words)
print(freq_dist_pos.most_common(10))
positive_tokens_for_model = get_tweets_for_model(positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset[:7000]
test_data = dataset[7000:]
# Classifier - TODO Add persistence
classifier = NaiveBayesClassifier.train(train_data)
print("Accuracy is:", classify.accuracy(classifier, test_data))
print(classifier.show_most_informative_features(100))
custom_tweet = "I ordered just once from TerribleCo, they screwed up, never used the app again."
custom_tokens = remove_noise(word_tokenize(custom_tweet))
print(custom_tweet, classifier.classify(dict([token, True] for token in custom_tokens)))
return classifier
def main():
custom_input = get_custom_input()
stop_words = stopwords.words('english')
custom_tokens = remove_noise(word_tokenize(custom_input), stop_words)
nltk_downloader()
sid = SentimentIntensityAnalyzer()
print("")
print(
"1: Sentiment Intensity Analysis: poitive, negative, neutral percentages"
)
print("Sentiment Intensity without noise removal")
ss = sid.polarity_scores(custom_input)
for k in sorted(ss):
print('{0}: {1}, '.format(k, ss[k]), end='')
print()
print("Sentiment Intensity with noise removal")
ss = sid.polarity_scores(" ".join(custom_tokens))
for k in sorted(ss):
print('{0}: {1}, '.format(k, ss[k]), end='')
print()
print("")
print('2: Basic Sentiment Analysis: Only Positive or Negative')
positive_tweets, negative_tweets, text = fetch_twitter_samples()
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = list()
negative_cleaned_tokens_list = list()
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
freq_dist_pos = FreqDist(get_all_words(positive_cleaned_tokens_list))
freq_dist_neg = FreqDist(get_all_words(negative_cleaned_tokens_list))
train_data, test_data = get_train_test_data(positive_cleaned_tokens_list,
negative_cleaned_tokens_list)
classifier = get_model_classifier(train_data, test_data,
NaiveBayesClassifier)
print("Result: ",
classifier.classify(dict([token, True] for token in custom_tokens)))
def train():
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
text = twitter_samples.strings('tweets.20150430-223406.json')
tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_pos_words = get_all_words(positive_cleaned_tokens_list)
freq_dist_pos = FreqDist(all_pos_words)
print(freq_dist_pos.most_common(10))
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset[:7000]
test_data = dataset[7000:]
classifier = NaiveBayesClassifier.train(train_data)
print("Accuracy is:", classify.accuracy(classifier, test_data))
print(classifier.show_most_informative_features(10))
f = open('classifier.pickle', 'wb')
pickle.dump(classifier, f)
f.close()
def train_twtr_classifier():
if os.path.isfile(SAVED_CLSR_LOC):
return load_classifier()
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
text = twitter_samples.strings('tweets.20150430-223406.json')
tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]
stop_words = stopwords.words('english')
pos_twt_toks = twitter_samples.tokenized('positive_tweets.json')
neg_twt_toks = twitter_samples.tokenized('negative_tweets.json')
positive_cleaned_tokens_list = [
remove_noise(toks, stop_words) for toks in pos_twt_toks
]
negative_cleaned_tokens_list = [
remove_noise(toks, stop_words) for toks in neg_twt_toks
]
all_pos_words = get_all_words(positive_cleaned_tokens_list)
freq_dist_pos = FreqDist(all_pos_words)
print(freq_dist_pos.most_common(10))
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset[:7000]
test_data = dataset[7000:]
classifier = NaiveBayesClassifier.train(train_data)
print("Accuracy is:", classify.accuracy(classifier, test_data))
save_classifier(classifier)
return classifier
def build_classifier():
print('reading data')
stop_words = stopwords.words('english')
positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')
print('cleaning tokens')
positive_cleaned_tokens_list = []
negative_cleaned_tokens_list = []
for tokens in positive_tweet_tokens:
positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
for tokens in negative_tweet_tokens:
negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))
all_pos_words = get_all_words(positive_cleaned_tokens_list)
print('building freq dist...')
freq_dist_pos = FreqDist(all_pos_words)
print(freq_dist_pos.most_common(10))
positive_tokens_for_model = get_tweets_for_model(
positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(
negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive")
for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative")
for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset[:7000]
test_data = dataset[7000:]
print('training...')
classifier = NaiveBayesClassifier.train(train_data)
print("Accuracy is:", classify.accuracy(classifier, test_data))
print(classifier.show_most_informative_features(10))
return classifier
def __init__(self):
"""
Gather data
"""
positive = twitter_samples.strings('positive_tweets.json')
negative = twitter_samples.strings('negative_tweets.json')
self.stop_words = list(set(stopwords.words('english')))
positive_tokens = twitter_samples.tokenized('positive_tweets.json')
negative_tokens = twitter_samples.tokenized('negative_tweets.json')
"""
Clean the data
"""
positive_clean = []
negative_clean = []
for token in positive_tokens:
positive_clean.append(self.clean(token))
for token in negative_tokens:
negative_clean.append(self.clean(token))
positive_model_tokens = self.final_token_generator(positive_clean)
negative_model_tokens = self.final_token_generator(negative_clean)
"""
Use generator to make datasets
"""
positive_dataset = [(token, "Positive")
for token in positive_model_tokens]
negative_dataset = [(token, "Negative")
for token in negative_model_tokens]
dataset = positive_dataset + negative_dataset
"""
Shake it all about
"""
random.shuffle(dataset)
random.shuffle(dataset)
random.shuffle(dataset)
"""
Split them up
"""
training = dataset[:7000]
testing = dataset[7000:]
"""
Train the classifier
"""
self.classifier = NaiveBayesClassifier.train(training)
"""
def read_input(infile, NUM_TRAIN, NUM_TEST):
train = []
test = []
pos_tweets = 0
neg_tweets = 0
for line in twitter_samples.tokenized("positive_tweets.json"):
sent = "Positive"
#Remove usernames, urls
for i, token in enumerate(line):
line[i] = re.sub("@[\S]+", "USERNAME", line[i])
line[i] = re.sub("www.[\S]+|https://[\S]+|http://[\S]+", "URL",
line[i])
newstr = ""
for ch in line[i]:
if ord(ch) > 128:
newstr += "EMOJI_{0}".format(ord(ch))
#print [ch], ord(ch)
else:
newstr += (ch)
line[i] = newstr
pos_tweets += 1
if pos_tweets < NUM_TRAIN:
train.append((line, sent))
else:
test.append((line, sent))
for line in twitter_samples.tokenized("negative_tweets.json"):
sent = "Negative"
neg_tweets += 1
#Remove usernames, urls
for i, token in enumerate(line):
line[i] = re.sub("@[\S]+", "USERNAME", line[i])
line[i] = re.sub("www.[\S]+|https://[\S]+", "URL", line[i])
newstr = ""
for ch in line[i]:
if ord(ch) > 128:
newstr += "EMOJI_{0}".format(ord(ch))
#print [ch], ord(ch)
else:
newstr += (ch)
line[i] = newstr
if neg_tweets < NUM_TRAIN:
train.append((line, sent))
else:
test.append((line, sent))
return test, train
def read_input(infile, NUM_TRAIN, NUM_TEST):
train = []
test = []
pos_tweets = 0
neg_tweets = 0
for line in twitter_samples.tokenized("positive_tweets.json"):
sent = "Positive"
#Remove usernames, urls
for i,token in enumerate(line):
line[i] = re.sub("@[\S]+", "USERNAME", line[i])
line[i] = re.sub("www.[\S]+|https://[\S]+|http://[\S]+", "URL", line[i])
newstr = ""
for ch in line[i]:
if ord(ch)>128:
newstr+= "EMOJI_{0}".format(ord(ch))
#print [ch], ord(ch)
else:
newstr+=(ch)
line[i] = newstr
pos_tweets+=1
if pos_tweets < NUM_TRAIN:
train.append((line, sent))
else:
test.append((line, sent))
for line in twitter_samples.tokenized("negative_tweets.json"):
sent = "Negative"
neg_tweets+=1
#Remove usernames, urls
for i,token in enumerate(line):
line[i] = re.sub("@[\S]+", "USERNAME", line[i])
line[i] = re.sub("www.[\S]+|https://[\S]+", "URL", line[i])
newstr = ""
for ch in line[i]:
if ord(ch)>128:
newstr+= "EMOJI_{0}".format(ord(ch))
#print [ch], ord(ch)
else:
newstr+=(ch)
line[i] = newstr
if neg_tweets < NUM_TRAIN:
train.append((line, sent))
else:
test.append((line, sent))
return test, train
def __init__(self):
self.stop_words = stopwords.words('english')
self.positive_cleaned_tokens_list = []
self.negative_cleaned_tokens_list = []
self.positive_tweets_tokens = twitter_samples.tokenized(
'positive_tweets.json')
self.negative_tweets_tokens = twitter_samples.tokenized(
'negative_tweets.json')
self.non_abusive = self.positive_tweets_tokens[:
808] + self.negative_tweets_tokens[:
811]
self.abusive_words = pd.read_csv('bad-words.csv')['jigaboo']
self.abusive = []
for word in self.abusive_words:
self.abusive.append(word)
def parseTweets(isNewData, tweets, posOrNeg):
print("inside parse")
allCleanedTokens = []
if (isNewData == True):
for tweet in tweets:
tokenizedTweet = tokenizeTweet(tweet)
cleanedTokens = removeNoise(tokenizedTweet,
stopwords.words('english'))
allCleanedTokens.append(cleanedTokens)
#wordsAllTweets = get_all_words(allCleanedTokens)
#print(FreqDist(wordsAllTweets).most_common(25))
tokensForModel = get_tweets_for_model(allCleanedTokens)
else:
tweets = 'positive_tweets.json' if (
posOrNeg == "positive") else 'negative_tweets.json'
tweet_tokens = twitter_samples.tokenized(tweets)
for tokens in tweet_tokens:
allCleanedTokens.append(
removeNoise(tokens, stopwords.words('english')))
tokensForModel = get_tweets_for_model(allCleanedTokens)
#wordsAllTweets = get_all_words(positive_cleaned_tokens_list)
#print(FreqDist(wordsAllTweets).most_common(25))
return tokensForModel
def twitter_token():
from nltk.corpus import twitter_samples
from nltk.tag import pos_tag_sents
tweets = twitter_samples.strings('positive_tweets.json')
tweets_tokens = twitter_samples.tokenized('positive_tweets.json')
tweets_tagged = pos_tag_sents(tweets_tokens)
"""
JJ:Adjective
singular nouns (NN)
plural nouns (NNS)
"""
JJ_count = 0
NN_count = 0
for tweet in tweets_tagged:
for key, tag in tweet:
#tag = pair[1]
if tag == 'JJ':
JJ_count += 1
elif tag == 'NN':
NN_count += 1
print('Total number of adjectives = ', JJ_count)
print('Total number of nouns = ', NN_count)
def corpusreader_demo():
"""
Use :module:`TwitterCorpusReader` tp read a file of tweets, and print out
* some full tweets in JSON format;
* some raw strings from the tweets (i.e., the value of the `text` field); and
* the result of tokenising the raw strings.
"""
from nltk.corpus import twitter_samples as tweets
print()
print("Complete tweet documents")
print(SPACER)
for tweet in tweets.docs("tweets.20150430-223406.json")[:1]:
print(json.dumps(tweet, indent=1, sort_keys=True))
print()
print("Raw tweet strings:")
print(SPACER)
for text in tweets.strings("tweets.20150430-223406.json")[:15]:
print(text)
print()
print("Tokenized tweet strings:")
print(SPACER)
for toks in tweets.tokenized("tweets.20150430-223406.json")[:15]:
print(toks)
def validate(self,classifier):
"""Test the accuracy of a given classifier against a test dataset with labels.
Args:
classifier: (Bayesain,DecisionTree,SVC,LinearSVC) for use in classifying data
Returns:
None
"""
tweets = twitter_samples.fileids()
pos_tweets = twitter_samples.tokenized(tweets[1])
neg_tweets = twitter_samples.tokenized(tweets[0])
pos_testing = pos_tweets[(len(pos_tweets)*7/8):]
neg_testing = neg_tweets[(len(neg_tweets)*7/8):]
pos_test = [(self.train_feats(f), 'positive') for f in pos_testing ]
neg_test = [(self.train_feats(f), 'negative') for f in neg_testing ]
testfeats = pos_test + neg_test
print("Classifier accuracy percent:",(nltk.classify.accuracy(classifier, testfeats))*100)
def create_and_train():
positive_tweets = twitter_samples.strings('positive_tweets.json')
negative_tweets = twitter_samples.strings('negative_tweets.json')
text = twitter_samples.strings('tweets.20150430-223406.json')
tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]
positive_cleaned_tokens_list = tokenize('positive_tweets.json')
negative_cleaned_tokens_list = tokenize('negative_tweets.json')
all_pos_words = get_all_words(positive_cleaned_tokens_list)
freq_dist_pos = FreqDist(all_pos_words)
positive_tokens_for_model = get_tweets_for_model(positive_cleaned_tokens_list)
negative_tokens_for_model = get_tweets_for_model(negative_cleaned_tokens_list)
positive_dataset = [(tweet_dict, "Positive") for tweet_dict in positive_tokens_for_model]
negative_dataset = [(tweet_dict, "Negative") for tweet_dict in negative_tokens_for_model]
dataset = positive_dataset + negative_dataset
random.shuffle(dataset)
train_data = dataset[:7000]
test_data = dataset[7000:]
classifier = NaiveBayesClassifier.train(train_data)
return classifier
def corpusreader_demo():
"""
Use `TwitterCorpusReader` tp read a file of tweets, and print out
* some full tweets in JSON format;
* some raw strings from the tweets (i.e., the value of the `text` field); and
* the result of tokenising the raw strings.
"""
from nltk.corpus import twitter_samples as tweets
print()
print("Complete tweet documents")
print(SPACER)
for tweet in tweets.docs("tweets.20150430-223406.json")[:1]:
print(json.dumps(tweet, indent=1, sort_keys=True))
print()
print("Raw tweet strings:")
print(SPACER)
for text in tweets.strings("tweets.20150430-223406.json")[:15]:
print(text)
print()
print("Tokenized tweet strings:")
print(SPACER)
for toks in tweets.tokenized("tweets.20150430-223406.json")[:15]:
print(toks)
def word_delegation():
positive = twitter_samples.tokenized('positive_tweets.json')
negative = twitter_samples.tokenized('negative_tweets.json')
tweets, all_words = [], []
for tweet in positive:
tweets.append((tweet, 'pos'))
for word in tweet:
all_words.append(word.lower())
for tweet in negative:
tweets.append((tweet, 'neg'))
for word in tweet:
all_words.append(word.lower())
word_features = list(nltk.FreqDist(all_words).keys())[:1000]
return tweets, word_features
from collections import Counter
from nltk.corpus import twitter_samples, stopwords
from nltk import bigrams
from nltk import trigrams
tokenizedWordsFromTwitter = twitter_samples.tokenized('tweets.20150430-223406.json')
tokenizedWordsFromTwitter += twitter_samples.tokenized('negative_tweets.json')
tokenizedWordsFromTwitter += twitter_samples.tokenized('positive_tweets.json')
#tokenizedWordsFromTwitter = twitter_samples.tokenized('positive_tweets.json')
for tweetIndex in range(len(tokenizedWordsFromTwitter)):
tokenizedWordsFromTwitter[tweetIndex] = [item for item in tokenizedWordsFromTwitter[tweetIndex] if item not in [".",",",":",";","!","?","(",")"]]
tokenizedWordsFromTwitter[tweetIndex] = [item for item in tokenizedWordsFromTwitter[tweetIndex] if item not in stopwords.words('english')]
print("Number of Tweets: " + str(len(tokenizedWordsFromTwitter)))
twitter_unigrams = Counter({})
twitter_bigrams = Counter({})
twitter_trigrams = Counter({})
for i in tokenizedWordsFromTwitter:
twitter_unigrams += Counter(i)
individual_bigrams = bigrams(i)
twitter_bigrams += Counter(individual_bigrams)
individual_trigrams = trigrams(i)
twitter_trigrams += Counter(individual_trigrams)
list_of_tokenized_words = [each_word for each_word in list_of_tokenized_words if each_word not in [",", ".", "..", "...", ":", "?", "!", "\'", "\"", "#", "-", "_", "(", ")"]]
list_of_tokenized_words = [stemmer.stem(each_word) for each_word in list_of_tokenized_words]
print(list_of_tokenized_words)
return list_of_tokenized_words
def word_feats(words):
return dict([(word, True) for word in get_words_from_sentence(words)])
def tokenize_sentence_into_list_of_words(sentence):
tknzr = TweetTokenizer()
list_of_tokenized_words = tknzr.tokenize(sentence)
return list_of_tokenized_words
negative_tokenizedWordsFromTwitter = twitter_samples.tokenized('negative_tweets.json')
positive_tokenizedWordsFromTwitter = twitter_samples.tokenized('positive_tweets.json')
negfeats = [(word_feats(f), 'neg') for f in negative_tokenizedWordsFromTwitter]
posfeats = [(word_feats(f), 'pos') for f in positive_tokenizedWordsFromTwitter]
negcutoff = int(len(negfeats)*3/4)
poscutoff = int(len(posfeats)*3/4)
trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]
print('train on %d instances, test on %d instances' % (len(trainfeats), len(testfeats)))
classifier = NaiveBayesClassifier.train(trainfeats)
print('accuracy:', nltk.classify.util.accuracy(classifier, testfeats))
print(classifier.show_most_informative_features())
from nltk.corpus import twitter_samples
from nltk.tag import pos_tag_sents
tweets = twitter_samples.strings('positive_tweets.json')
tweets_tokens = twitter_samples.tokenized('positive_tweets.json')
JJ_count = 0
NN_count = 0
tweets_tagged = pos_tag_sents(tweets_tokens)
for tweet in tweets_tagged:
for pair in tweet:
tag = pair[1]
if tag == 'JJ':
JJ_count += 1
elif tag == 'NN':
NN_count += 1
print('Total number of adjectives = ', JJ_count)
print('Total number of nouns = ', NN_count)
import nltk
import plotly.plotly as py
import plotly.graph_objs as go
from tqdm import tqdm
# These are corpus files. One positive, one negative and one mixed
print twitter_samples.fileids()
# Empty dictionaries to store the collected tags
posTags = {}
negTags = {}
# The corpuses are already POS tagged
# Load the positive tweets and load the POS tags into 'posTags'
tokenized = twitter_samples.tokenized('positive_tweets.json')
print "Loaded"
for toks in tqdm(tokenized):
toks = nltk.pos_tag(toks)
for word in toks:
if word[1] in posTags:
posTags[word[1]] += 1
else:
posTags[word[1]] = 1
# Load the negative tweets and load the POS tags into 'negTags'
tokenized = twitter_samples.tokenized('negative_tweets.json')
for toks in tqdm(tokenized):
toks = nltk.pos_tag(toks)
for word in toks:
if word[1] in negTags:
