def chat(request):
custs = Customer.objects.all()
consumer_key = 'q3NopxIzKRa7cXvPGXCInkFee'
consumer_secret = 'TcvYUi4oIiDOonBO0crCoy4D3UjdjHpY0r8cjSpnZyMl6UP4H8'
access_token = '1254647674160078848-32NJL0yJcmKx3y9n8eQht0d7pUtzNt'
access_token_secret = 'zsVUrFkxLqMGABg1VopYqKKEdThb25tNaNXYprPx9ewuh'
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
for names in custs:
polarity = 0
subjectivity = 0
name = str(names.name)
public_tweets = api.search(name)
print(name)
for tweet in public_tweets:
analysis = TextBlob(tweet.text)
Sentiment = analysis.sentiment
polarity += Sentiment.polarity
subjectivity += Sentiment.subjectivity
names.pol = polarity
names.sub = subjectivity
names.save()
return render(request, 'chat.html', {'custs': custs})
def chat(request):
custs = Customer.objects.all()
consumer_key = 'midEzHb8nacvmLGo1c2fmita9'
consumer_secret = 'nEx2a7AU1N31laL82pr9fK2B1jdLgrtQfxWTVt2II4CetapSwz'
access_token = '1254277768449347586-Wo5FXro68ailMEV4mMiZRIVNRDT9cY'
access_token_secret = 's2EJVlVO30RKlsJ7jT1hUVOVb3zDOvYhJg9KOxC4kNcx3'
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
for names in custs:
polarity = 0
subjectivity = 0
name = str(names.name)
public_tweets = api.search(name)
print(name)
for tweet in public_tweets:
analysis = TextBlob(tweet.text)
Sentiment = analysis.sentiment
polarity += Sentiment.polarity
subjectivity += Sentiment.subjectivity
names.pol = polarity
names.sub = subjectivity
names.save()
return render(request, 'chat.html', {'custs': custs})
def sentimental(request):
subjectivity = 0
polarity = 0
customers = Customers.objects.all()
for customer in customers:
print("inside loop")
consumer_key = 'I5uJYivxN2sZRuz7JbyqIU0dg'
consumer_secret = 'tO8kCyFHb0HwPZeq8Vvz2z9eEUc4xSXD56QhmzqrWiDSRbuahz'
access_token = '1249729070671130624-xme25T1yhnLWHwQM9ECuJIN9u7fJss'
access_token_secret = 'J67rF7bVEGvBdLEwGBs20Jgl0Nb59JNAakobSiocrTmAP'
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
public_tweets = api.search(customer.name)
print("after search operation")
for tweet in public_tweets:
print(tweet.text)
analysis = TextBlob(tweet.text)
polarity = polarity + analysis.polarity
subjectivity = subjectivity + analysis.subjectivity
print(analysis.sentiment)
print("analysis is printed above")
customer.polarity = polarity
customer.subjectivity = subjectivity
customer.save()
print("after save")
return render(request, 'tweet.html', {'customers': customers})
def getTweets():
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
public_tweets = api.home_timeline(count=10)
tweets = []
for tweet in public_tweets:
status = tweet.text
tweets.append({'status': status})
return {'tweets': tweets}
def postTweet(up_status):
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
api.update_status(up_status)
def fakeornot(request):
auth = tweepy.OAuthHandler(
"x4yJNIGwJBspKCD3jXps0udJm",
"Xzi5PvN4rHvqBr6kPgcUCiSRDLLGLFJDFRZ1gPAvXxv5kvhyZZ")
auth.set_access_token("2491739339-q9jEkfotc22ETdAKFGNWiKLY7NCAtmy5cMbbMXI",
"UqdiqHDl1h5czl9jxpgDZQezm9mw88hA3qzX3Q72yHTA1")
api = tweepy.API(auth)
names = [
'twtCount', 'engRatio', 'frndcount', 'biolen', 'likecount', 'verified',
'default', 'class'
]
ds = pandas.read_csv("static/dataset_original.csv", names=names)
print("shape: {}".format(ds.shape))
array = ds.values
X = array[:, 0:7]
Y = array[:, 7]
X_train, X_validation, Y_train, Y_validation = train_test_split(
X, Y, test_size=0.30, random_state=None)
print("x shape: {}".format(X_train.shape))
print("y shape: {}".format(Y_train.shape))
print("x test shape: {}".format(X_validation.shape))
print("y test shape: {}".format(Y_validation.shape))
print("x_train:{}".format(X_train))
print("X_test:{}".format(X_validation))
# Spot Check Algorithms
models = []
models.append(
('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC(gamma='auto')))
# evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = StratifiedKFold(n_splits=5, random_state=None)
cv_results = cross_val_score(model,
X_train,
Y_train,
cv=kfold,
scoring='accuracy')
results.append(cv_results)
names.append(name)
print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))
lda = LinearDiscriminantAnalysis()
lda.fit(X_train, Y_train)
p = request.POST['name1']
user = api.get_user(p)
tweet = user.status
endDate = datetime.datetime(2020, 5, 14, 0, 0, 0)
startDate = datetime.datetime(2020, 5, 8, 0, 0, 0)
tweets = []
tmpTweets = api.user_timeline(p)
for tweet in tmpTweets:
if (tweet.created_at < endDate and tweet.created_at > endDate):
tweets.append(tweet.favorite_count)
while (tmpTweets[-1].created_at > startDate):
tmpTweets = api.user_timeline(p, max_id=tmpTweets[-1].id)
for tweet in tmpTweets:
if tweet.created_at < endDate and tweet.created_at > startDate:
tweets.append(tweet.favorite_count)
if (user.verified) == True:
a = 1
else:
a = 0
if (user.default_profile) == True:
b = 0
else:
b = 1
if (len(tweets) == 0):
t = user.status.favorite_count
else:
tweets.sort()
t = tweets[-1]
X_new = np.array([[
user.statuses_count, (t / user.followers_count) * 1000,
user.friends_count,
len(user.description), user.favourites_count, a, b
]])
prediction = lda.predict(X_new)
q = prediction
print(prediction)
if q == ['not']:
v = 0
return render(request, 'not.html', {
'result': 'This is a genuine twitter account',
'l': p
})
else:
v = 1
auth = tweepy.OAuthHandler(
"x4yJNIGwJBspKCD3jXps0udJm",
"Xzi5PvN4rHvqBr6kPgcUCiSRDLLGLFJDFRZ1gPAvXxv5kvhyZZ")
auth.set_access_token(
"2491739339-q9jEkfotc22ETdAKFGNWiKLY7NCAtmy5cMbbMXI",
"UqdiqHDl1h5czl9jxpgDZQezm9mw88hA3qzX3Q72yHTA1")
api = tweepy.API(auth)
user = api.get_user(p)
tweet = user.status
# Loading the data set - training data.
from sklearn.datasets import fetch_20newsgroups
twenty_train = fetch_20newsgroups(subset='train', shuffle=True)
# In[6]:
# Extracting features from text files
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(twenty_train.data)
X_train_counts.shape
# In[7]:
# TF-IDF
from sklearn.feature_extraction.text import TfidfTransformer
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)
X_train_tfidf.shape
# In[15]:
# Performance of NB Classifier
import numpy as nv
from sklearn.pipeline import Pipeline
twenty_test = fetch_20newsgroups(subset='test', shuffle=True)
# Training Support Vector Machines - SVM and calculating its performance
from sklearn.linear_model import SGDClassifier
text_clf_svm = Pipeline([('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf-svm',
SGDClassifier(loss='hinge',
penalty='l2',
alpha=1e-3,
random_state=42))])
text_clf_svm = text_clf_svm.fit(twenty_train.data, twenty_train.target)
input = user.description
input = [input]
pr = text_clf_svm.predict(input)
res = str(pr)[1:-1]
print("belongs to the category : " +
twenty_train.target_names[int(res)])
return render(request, 'not2.html',
{'result': twenty_train.target_names[int(res)]})
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import BernoulliNB
##<--- setting connection with the twitter API----->##
temp=[]
credentials = {}
credentials['CONSUMER_KEY'] = "YoknJGgd0DYnUe4m7Z4gSDTK3"
credentials['CONSUMER_SECRET'] = "RU83Rq7hg2aKreuzZzAdzvR9QFCQfk0YMauJPRwI5D8GvgOg4g"
credentials['ACCESS_TOKEN'] = "1275391205598277633-dZD2nyYAqQG3Pb1avTIBvhfsQy0Q6G"
credentials['ACCESS_SECRET'] = "JQz2x78eetOPrFYYh8guwuLtsbtADD2f9zcnxYKddNiNo"
with open("twitter_credentials.json", "w") as file:
json.dump(credentials, file)
auth = tw.OAuthHandler(credentials['CONSUMER_KEY'], credentials['CONSUMER_SECRET'])
auth.set_access_token(credentials['ACCESS_TOKEN'], credentials['ACCESS_SECRET'])
api = tw.API(auth, wait_on_rate_limit=True)
python_tweets = Twython(credentials['CONSUMER_KEY'], credentials['CONSUMER_SECRET'])
list_of_objects=[]
queryrequest=[]
words=[]
length_of_data = -1
stop = stopwords.words('english')
nlp = spacy.load("en_core_web_sm")
#try:
# model = joblib.load('randomforest.joblib')
#vectorizer = joblib.load('vectorizer.joblib')
#reslt = joblib.load('resutl.joblib')
#print('resultant was ', reslt)