def del_a_comment(insta_username):
media_id = get_post_id(insta_username)
url = BASE_URL + "/media/media-id/comments?access_token={}".format(
ACCESS_TOKEN)
print(url)
comment_info = requests.get(url).json()
if comment_info['meta']['code'] == 200:
if len(comment_info['data']):
#Here's a naive implementation of how to delete the negative comments :
for x in range(0, len(comment_info["data"])):
comment_id = comment_info['data'][x]['id']
comment_text = comment_info['data'][x]['text']
blob = TextBlob(comment_text, analyzer=NaiveBayesAnalyzer())
if (blob.sentiment.p_neg > blob.sentiment.p_pos):
print('Negative comment : {}').format(comment_text)
delete_url = BASE_URL + "/media/{}/comments/{}/?access_token={}".format(
media_id, comment_id, ACCESS_TOKEN)
print('DELETE request url : %s').format(delete_url)
delete_info = requests.delete(delete_url).json()
if delete_info['meta']['code'] == 200:
print('Comment successfully deleted!\n')
else:
print('Unable to delete comment!')
else:
print('Positive comment : %s\n').format(comment_text)
else:
print('There are no existing comments on the post!')
else:
print('Status code other than 200 received!')
def get_sentiment_for_text(text):
'''
Returns the sentiment score for the given text.
'''
text_analyzer = TextBlob(text, analyzer=NaiveBayesAnalyzer())
sentiment = text_analyzer.sentiment
return (sentiment.classification, sentiment.p_pos, sentiment.p_neg)
def __init__(self, chunk: ('page', 'sentence'), verbose=True):
self.chunk = chunk
self.verbose = verbose
self.logger = self.get_logger()
self.scorers = {'vader': SentimentIntensityAnalyzer(),
'pattern': PatternAnalyzer(),
'huggingface': pipeline('sentiment-analysis'),
'naive': Blobber(analyzer=NaiveBayesAnalyzer())}
def __init__(self, load_classifier=False):
if load_classifier:
self._load_classifier()
print("classifier loaded !")
self.tb = Blobber(analyzer=NaiveBayesAnalyzer())
self.stop = set(stopwords.words('french'))
self.stop_en = set(stopwords.words('english'))
def get_tweet_sentiment_NBA(tweet):
'''
Utility function to classify sentiment of passed tweet
using textblob's sentiment method
'''
# create TextBlob object of passed tweet text
analysis = TextBlob(clean_tweet(tweet), analyzer=NaiveBayesAnalyzer())
# set sentiment
return analysis.sentiment
def calculate_and_persist_subs_sentiment():
for row in rows:
print(row[1], row[2])
pattern_analyser = TextBlob(row[2])
naive_bayes_analyser = TextBlob(row[2], analyzer=NaiveBayesAnalyzer())
cursor.execute(
f'UPDATE Video SET '
f'PA = {pattern_analyser.sentiment.polarity}, '
f'NBA = {naive_bayes_analyser.sentiment.p_pos} WHERE VIDEO_ID = \'{row[1]}\''
)
def sentiment_analysis(text, nb=False):
if nb:
sa = TextBlob(text, analyzer=NaiveBayesAnalyzer()).sentiment
return {
"class": sa.classification,
"p_pos": sa.p_pos,
"p_neg": sa.p_neg
}
sa = TextBlob(text).sentiment
sa_class = "pos" if sa.polarity >= 0 else "neg"
return {
"class": sa_class,
"polarity": sa.polarity,
"subjectivity": sa.subjectivity
}
naive = load_naiveclassifier()
else:
naive = NaiveBayesClassifier(train)
save_naiveclassifier(naive)
print "Naive Bayes Trained"
if os.path.exists('/home/lakeesh10/Documents/projectdemo/decisiontree_classifier.pickle'):
decision = load_decisionclassifier()
else:
decision = DecisionTreeClassifier(train)
save_decisionclassifier(decision)
print "Decision Tree Trained"
print("Naive Bayes : ",naive.classify("fried chip good and crunchy dig thattaco tropical omg so eyeopening"))
#print(decision.classify("fried chip good and crunchy dig thattaco tropical omg so eyeopening"))
cl=NaiveBayesAnalyzer()
print (cl.analyze("fried chip good and crunchy dig thattaco tropical omg so eyeopening"))
blob = TextBlob("fried chip good and crunchy dig thattaco tropical omg so eyeopening")
polarity=0
i=0
for sentence in blob.sentences:
polarity=polarity+sentence.sentiment.polarity
i=i+1
polarity=polarity/i
print(polarity)
negids = movie_reviews.fileids('neg')
posids = movie_reviews.fileids('pos')
negfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'neg') for f in negids]
posfeats = [(word_feats(movie_reviews.words(fileids=[f])), 'pos') for f in posids]
tweet_text)
return map_user_to_tweets
if __name__ == '__main__':
text_tweet = "This is Rachel Crooks. In 2005 she accused Donald Trump of kissing her on the mouth without her permission. Now she is running for the state legislature in Ohio. https://t.co/BiFKQe6WVh"
normal = "RT @realDonaldTrump: Justice Ginsburg of the U.S. Supreme Court has embarrassed all by making very dumb political statements about me. Her…"
text = "RT @gitagatubixi #Trump Sing along with us: 🎶Better not do us wrong!🎺 https://t.co/NPhXbfZ92g"
pp = preprocess_tweet_text_advanced(text)
# re.sub(r'RT @[^ ]*?:', '', text, count=1, flags=re.MULTILINE)
tt = preprocess_tweet_text_advanced(text_tweet)
blobber = Blobber(analyzer=NaiveBayesAnalyzer())
words_number, polarity, subjectivity, tag = stanford_nltk_blob_analysis(
text_tweet, blobber)
x = 5
tweets_texts_list = [
'RT Trump Sing along with us: 🎶Better not do us wrong!🎺',
'RT Trump Sing along with us: 🎶Better not do us wrong!🎺',
'RT LorettoRegina Trump Sing along with us: 🎶We honor our veterans!🎺',
'RT WilliamRolar Trump Sing along with us: 🎶Be bad, you’ll get banned!🎺'
]
session, embedded_placeholder, placeholder = prepare_tensorflow_graph_and_session(
)
check_message_similatiry(session, embedded_placeholder, placeholder,
tweets_texts_list, 0.8)