def sent_analysis():
positive = 0
negative = 0
neutral = 0
query()
from paralleldots import set_api_key, sentiment
# Setting API key
set_api_key("F6IhnjekXoKsgzOwy1ZsGCX6ph76YK5F6SzFf968gOk")
#Viewing API key
paralleldots.get_api_key()
for tweet in tweets:
tweet_text = tweet.text
sentiment_type = sentiment(tweet_text)
sentiment_values = sentiment_type['sentiment']
if sentiment_values == "positive":
positive = positive + 1
elif sentiment_values == "negative":
negative = negative + 1
else:
neutral = negative + 1
if positive > negative and positive > neutral:
print("POSITIVE SENTIMENT with count" + " " + str(positive))
elif negative > positive and negative > neutral:
print("NEGATIVE SENTIMENT with count" + " " + str(negative))
else:
print("NEUTRAL SENTIMNET with count" + " " + str(neutral))
def sent_analysis():
positive = 0
negative = 0
neutral = 0
query()
from paralleldots import set_api_key, sentiment
set_api_key("")
paralleldots.get_api_key()
for tweet in tweets:
tweet_text = tweet.text
sentiment_type = sentiment(tweet_text)
sentiment_values = sentiment_type['sentiment']
if sentiment_values == "positive":
positive = positive + 1
elif sentiment_values == "negative":
negative = negative + 1
else:
neutral = negative + 1
if positive > negative and positive > neutral:
print("POSITIVE SENTIMENT with count" + " " + str(positive))
elif negative > positive and negative > neutral:
print("NEGATIVE SENTIMENT with count" + " " + str(negative))
else:
print("NEUTRAL SENTIMNET with count" + " " + str(neutral))
def get_tweets(username):
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
tweets = api.user_timeline(screen_name=username, count=20)
tmp = []
tweets_for_csv = [tweet.text for tweet in tweets] # CSV file created
for j in tweets_for_csv:
tmp.append(j)
var1 = 0
var2 = 0
var3 = 0
print(tmp)
from paralleldots import set_api_key, get_api_key, sentiment
set_api_key("6dm9k0RomplpimtZETEkwp6JzMTrPSDhhMIiGPGmu68")
get_api_key()
for t in tmp:
a = sentiment(t)
print(t, "-->", a)
time.sleep(1)
if a['sentiment'] == 'positive':
var1 += 1
if a['sentiment'] == 'negative':
var2 += 1
if a['sentiment'] == 'neutral':
var3 += 1
if (var1 > var2) and (var1 > var3):
print("This user is positive on Twitter")
if (var2 > var3) and (var2 > var1):
print("This user is negative on Twitter")
if (var3 > var2) and (var3 > var1):
print("This user is neutral on Twitter")
def get_sentiments(query):
p = 0
n = 0
ne = 0
set_api_key('2Z4UlTNyfjXwIn5CGLy4EvS5IaySrLFfJDiMSPGCo3o')
get_api_key()
public_tweets = api.search(query)
for tweet in public_tweets:
text = tweet.text
print(
colored(
"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++",
color='blue'))
print(colored(tweet.text, color='red'))
r = sentiment(tweet.text)
print(colored(r, color='red'))
result = r['sentiment']
if result == "positive":
p = p + 1
elif r['sentiment'] == "neutral":
n = n + 1
else:
ne = ne + 1
print(
colored(
"+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++",
color='green'))
print "Maximum positive comments: ", p
print "Maximum neutral comments: ", n
print "Maximum negative comments: ", ne
print(
colored(
"+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++",
color='green'))
def sentimentAnalysis():
positive_sentiment=0;
negative_sentiment=0;
query()
from paralleldots import set_api_key, get_api_key,sentiment
set_api_key("8dyQhJPFerUALsn2lBpMAftocXOIr6bAFb6vJcrEYYM")
get_api_key()
for tweet in tweets:
txt = tweet.text
sentiment_value = sentiment(txt)
value = sentiment_value['sentiment']
if value == "positive":
positive_sentiment = positive_sentiment + 1
else:
negative_sentiment = negative_sentiment + 1
if positive_sentiment > negative_sentiment :
print("Sentiment is Positive ")
else:
print("Sentiment is Negative")
def Determine_the_sentiment():
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
username = input("enter any user id:")
tweets = api.user_timeline(screenname=username, count=20)
tmp = []
tweets_for_csv = [tweet.text for tweet in tweets]
for j in tweets_for_csv:
tmp.append(j)
pos = 0
neg = 0
neu = 0
print(tmp)
from paralleldots import set_api_key, get_api_key, sentiment
set_api_key("2S3zRrv1jxndgO6NQ989I4iJlEU8PHD1aOaAvCM4kw8")
get_api_key()
for t in tmp:
a = sentiment(t)
if a['sentiment'] == 'positive':
pos += 1
if a['sentiment'] == 'negative':
neg += 1
if a['sentiment'] == 'neutral':
neu += 1
if (pos > neg) and (pos > neu):
print("postive")
if (neg > neu) and (neg > pos):
print("negative")
if (neu > neg) and (neu > pos):
print("neutral")
def sentiment_analysis():
flagp = 0
flagn = 0
flagneg = 0
query()
from paralleldots import set_api_key, get_api_key
from paralleldots import similarity, ner, taxonomy, sentiment, keywords, intent, emotion, abuse, multilang_keywords
set_api_key("")
get_api_key()
for tweet in tweets:
text = tweet.text
sentiment_value = sentiment(text)
values1 = sentiment_value['sentiment']
if values1 == "positive":
flagp = flagp + 1
elif values1 == "negative":
flagneg = flagneg + 1
else:
flagn = flagn + 1
if flagn > flagneg and flagn > flagp:
print("Sentiment: Neutral")
elif flagneg > flagn and flagneg > flagp:
print("Sentiment: Negative")
else:
print("Sentiment: Positive")
def sixth():
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
username = input("enter any user id:")
tweets = api.user_timeline(screenname=username, count=20)
tmp = []
tweets_for_csv = [tweet.text for tweet in tweets]
for j in tweets_for_csv:
tmp.append(j)
flotpos = 0
flotneg = 0
flotneu = 0
print(tmp)
from paralleldots import set_api_key, get_api_key, sentiment
set_api_key("60TE8tX8lV1KIy8OhpGEUpLRa4RvyJaXA7IsIEXt6x4")
get_api_key()
for t in tmp:
a = sentiment(t)
if a['sentiment'] == 'positive':
flotpos += 1
if a['sentiment'] == 'negative':
flotneg += 1
if a['sentiment'] == 'neutral':
flotneu += 1
if (flotpos > flotneg) and (flotpos > flotneu):
print("postive")
if (flotneg > flotneu) and (flotneg > flotpos):
print("negative")
if (flotneu > flotneg) and (flotneu > flotpos):
print("neutral")
def get_tweets(username): #sentimental analysis
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
tweets = api.user_timeline(screen_name=username, count=20)
tmp = []
tweets_for_csv = [tweet.text for tweet in tweets] # CSV file created
for j in tweets_for_csv:
tmp.append(j) # store the tweets in tmp list
var1 = 0
var2 = 0
var3 = 0
print(tmp)
from paralleldots import set_api_key, get_api_key, sentiment
set_api_key("6dm9k0RomplpimtZETEkwp6JzMTrPSDhhMIiGPGmu68")
get_api_key()
for t in tmp:
a = sentiment(t)
print(a)
if a['sentiment'] == 'positive': #checking positive tweets
var1 += 1
if a['sentiment'] == 'negative': #checking negative tweets
var2 += 1
if a['sentiment'] == 'neutral': #checking neutral tweets
var3 += 1
if (var1 > var2) and (var1 >
var3): #checking the person is positive or not
print("positive")
if (var2 > var3) and (var2 >
var1): #checking the person is negative or not
print("negative")
if (var3 > var2) and (var3 >
var1): #checking the person is neutrl or not
print("neutral")
def sentimental():
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
username = input("enter any user id:")
tweets = api.user_timeline(screenname=username, count=20)
tmp = []
tweets_for_csv = [tweet.text for tweet in tweets]
for j in tweets_for_csv:
tmp.append(j)
count1 = 0
count2 = 0
count3 = 0
print(tmp)
from paralleldots import set_api_key, get_api_key, sentiment
set_api_key("M6aheAI13WZLXrxV9Gv3rsm8Fc8kXYKuYapZ7n2G8Wo")
get_api_key()
for t in tmp:
a = sentiment(t)
if a['sentiment'] == 'positive':
count1 += 1
if a['sentiment'] == 'negative':
count2 += 1
if a['sentiment'] == 'neutral':
count3 += 1
if (count1 > count2) and (count1 > count3):
print("postive")
if (count2 > count3) and (count2 > count1):
print("negative")
if (count3 > count2) and (count3 > count1):
print("neutral")
def get_highest_two_emotions(text):
paralleldots.set_api_key(paralleldots_TOKEN)
paralleldots.get_api_key()
emotions = paralleldots.emotion(text)["emotion"]
my_list = [k for k, v in emotions.items() if v == max(emotions.values())]
if my_list[0] == "Fear":
return "Sad"
return my_list[0]
def analyze_text_w(text):
paralleldots.set_api_key(paralleldots_TOKEN)
paralleldots.get_api_key()
emotions = paralleldots.emotion(text)["emotion"]
pos = (emotions["Happy"] + emotions["Excited"]) / 2
neg = (emotions["Angry"] + emotions["Bored"] + emotions["Fear"] +
emotions["Sad"]) / 4
print(pos, " ", neg)
def __init__(self):
if os.path.isfile('settings.cfg'):
""" If settings file already exists then no need to set up api key,
this file is automatically generated by the set and get api key
functions."""
print("Settings file already exists with appropriate API details.")
else:
# Setup api keys, this creates the settings.cfg file.
set_api_key("") # Please enter your API key here.
get_api_key()
def sentiment_analysis():
set_api_key(key_list)
get_api_key()
user = api.get_user('vivek_shivam007')
stuff = api.user_timeline(screen_name='ippatel',
count=10,
include_rts=True)
print("Sentiment Analysis Result:-")
for status in stuff:
text = status.text
sentiment_value = sentiment(text)
print(sentiment_value['sentiment'])
def sentiment_analysis(tweets):
set_api_key("4U0rm3Hboel2L0HqxMPvErNT67FQZvr4gBrxwrY1geg")
get_api_key()
sentiment_ana = []
for text in tweets:
value = sentiment(text)
sentiment_value = sentiment(text)
try:
values1 = sentiment_value['sentiment']
sentiment_ana.append(values1)
except:
sentiment_ana.append("don't know")
return sentiment_ana
def __init__(self, tweets):
"""TweetsAnalysis Class Constructor.
:param tweets: List of JSON objects containing certain user's tweets.
"""
self.tweets = tweets
# Setting ParalleDots API keys
set_api_key(open('D:/TwitterScraper/ParallelDotsKey.txt', 'r').read())
get_api_key()
#Dictionary to save analysis results
self.emotions = {}
def run(text, lib='textblob'):
try:
if lib == 'paralleldot':
# Setting your API key
paralleldots.set_api_key(Config.PARALLEL_DOT_API_KEY)
# Viewing your API key
paralleldots.get_api_key()
sentiment = paralleldots.sentiment(text)['sentiment']
return max(sentiment, key=sentiment.get)
else:
testimonial = TextBlob(text)
return testimonial.sentiment.polarity
except Exception as e:
print(e)
return 'neutral'
def get_sentiments(query):
p = 0
n = 0
ne = 0
set_api_key('S97T8CaNZyl5jDc5HeKh8Xe6MxldTdNL9D4CKbcRy5o')
get_api_key()
public_tweets = api.search(query)
for tweet in public_tweets:
text = tweet.text
print(colored(tweet.text, color='green'))
r = sentiment(tweet.text)
print(colored(r, color='green'))
result = r['sentiment']
if result == "positive":
positive = p + 1
elif r['sentiment'] == "neutral":
n = n + 1
else:
ne = ne + 1
print "Maximum positive comments: ", positive
print "Maximum neutral comments: ", n
print "Maximum negative comments: ", ne
def get_sentiments(query):
p = 0
n = 0
ne = 0
set_api_key('YjTdGL5qoPMia9yaiziaUjTL2uEvRWFBFu3Rp0sdbd0')
get_api_key()
public_tweets = api.search(query)
for tweet in public_tweets:
text = tweet.text
print(
colored(
"*****************************************************************************************"
"********************************************************************************************",
color='green'))
print(colored(tweet.text, color='red'))
r = sentiment(tweet.text)
print(colored(r, color='red'))
result = r['sentiment']
if result == "positive":
p = p + 1
elif r['sentiment'] == "neutral":
n = n + 1
else:
ne = ne + 1
print(
colored(
"*****************************************************************************************"
"********************************************************************************************",
color='blue'))
print "Maximum positive comments: ", p
print "Maximum neutral comments: ", n
print "Maximum negative comments: ", ne
print(
colored(
"*****************************************************************************************"
"********************************************************************************************",
color='blue'))
def get_sentiments(query):
p = 0
n = 0
ne = 0
set_api_key('nevysH2HFB0VkHllFav0tUJitebNhLvzU0O5IM9cOTc')
get_api_key()
public_tweets = api.search(query)
for tweet in public_tweets:
text = tweet.text
print(
colored(
"-----------------------------------------------------------------------------------------"
"----------------------------------------------------------------------------------------------",
color='green'))
print(colored(tweet.text, color='blue'))
r = sentiment(tweet.text)
print(colored(r, color='red'))
result = r['sentiment']
if result == "positive":
p = p + 1
elif r['sentiment'] == "neutral":
n = n + 1
else:
ne = ne + 1
print(
colored(
"------------------------------------------------------------------------------------------"
"-----------------------------------------------------------------------------------------------",
color='cyan'))
print "Maximum positive comments: ", p
print "Maximum neutral comments: ", n
print "Maximum negative comments: ", ne
print(
colored(
"-----------------------------------------------------------------------------------------"
"----------------------------------------------------------------------------------------------",
color='cyan'))
def get_sentiments(query):
p = 0
n = 0
ne = 0
set_api_key('kQmnPQVgdkzuRHaPpQJgqXLAmWlYV6GCx62z8LuitUQ')
get_api_key()
public_tweets = api.search(query)
for tweet in public_tweets:
text = tweet.text
print(
colored(
"*****************************************************************************************"
"********************************************************************************************",
color='green'))
print(colored(tweet.text, color='blue'))
r = sentiment(tweet.text)
print(colored(r, color='red'))
result = r['sentiment']
if result == "positive":
p = p + 1
elif r['sentiment'] == "neutral":
n = n + 1
else:
ne = ne + 1
print(
colored(
"*****************************************************************************************"
"********************************************************************************************",
color='red'))
print "Maximum positive comments: ", p
print "Maximum neutral comments: ", n
print "Maximum negative comments: ", ne
print(
colored(
"*****************************************************************************************"
"********************************************************************************************",
color='red'))
def sentiment_analysis():
flagpos = 0
flagneu = 0
flagneg = 0
query()
from paralleldots import similarity, taxonomy, sentiment, emotion, abuse
set_api_key("fyumEzmg3hQyh3jz6Dhquuf9CYaVPB3TfrpJoR5Mhrs")
get_api_key()
for tweet in tweets:
text = tweet.text
sentiment_value = sentiment(text)
values1 = sentiment_value['sentiment']
if values1 == "positive":
flagpos = flagpos + 1
elif values1 == "negative":
flagneg = flagneg + 1
else:
flagneu = flagneu + 1
if flagneu > flagneg and flagneu > flagpos:
print("Sentiment: Neutral")
elif flagneg > flagneu and flagneg > flagpos:
print("Sentiment: Negative")
else:
print("Sentiment: Positive")
def sentiment_analysis():
twpos = 0
twneu = 0
twneg = 0
query()
from paralleldots import similarity, taxonomy, sentiment, emotion, abuse
set_api_key("")
get_api_key()
for tweet in tweets:
text = tweet.text
sentiment_value = sentiment(text)
values1 = sentiment_value
if values1 == "positive":
twpos = twpos + 1
elif values1 == "negative":
twneg = twneg + 1
else:
twneu = twneu + 1
if twneu > twneg and twneu > twpos:
print("Sentiment: Neutral")
elif twneg > twneu and twneg > twpos:
print("Sentiment: Negative")
else:
print("Sentiment: Positive")
def is_abusive(text):
# import pdb ;
# pdb.set_trace()
url = 'http://apis.paralleldots.com/v3/abuse'
api_key = paralleldots.get_api_key()
response = requests.post("https://apis.paralleldots.com/v3/abuse",
data={
"api_key": api_key,
"text": text
}).text
# payload = {'apikey': PKEY, 'text': text}
text_type = json.loads(response)
#text_type = requests.post(url, payload).json()
if text_type['sentence_type'] == 'Abusive':
print(text_type['confidence_score'])
print(text_type['sentence_type'])
return True
else:
return False
from keys import consumer_key, consumer_secret, access_token, access_secret
import tweepy
from paralleldots import set_api_key, get_api_key
from paralleldots import *
import nltk
from nltk.corpus import *
from collections import Counter
nltk.download('stopwords')
stop_word = set(stopwords.words('english'))
set_api_key("IOEJcuuQyQwF0R1Ii7BbZO3jadNBpjVPsdaZLYdNJo4")
get_api_key()
oauth = tweepy.OAuthHandler(consumer_key, consumer_secret)
oauth.set_access_token(access_token, access_secret)
api = tweepy.API(oauth)
def menu():
print('''1:-Retrieve Tweets
2:-Count the followers
3:-Determine the sentiment
4:-Determine location,language and time zone.
5:-Compare tweets
6:-Analyze top usage
7:-Tweet a message
8:-exit
''')
def tweets():
q = input("enter what you want : ")
search_results = api.search(q)
# Import
import paralleldots
import csv
# Setting your API key
paralleldots.set_api_key("API KEY")
# Viewing your API key
paralleldots.get_api_key()
with open('Training document', mode='r', encoding="utf8") as csv_file:
csv_reader = csv.DictReader(csv_file)
line_count = 0
for row in csv_reader:
if line_count == 0:
print(f'Column names are {", ".join(row)}')
line_count += 1
#print(f'\nID: {row["ID"]} \n\t with text: {row["Text"]} \n')
lines = {row["Text"]}
print('\nThe intent analysis results for' + '\033[1m' + ' ID: ' +
f'{row["ID"]}' + '\033[0m' + ' in the file:')
print("---------------------------------------------------------\n")
response = paralleldots.intent(lines)
print(response)
line_count += 1
def take_picture_with_camera():
paralleldots.set_api_key("br13ubwK9UvtgVahL09oDrw2KxLtRGKygrgonAmLqjY")
paralleldots.get_api_key()
camera.start_preview()
#camera.rotation(180)
sleep(0.5)
camera.capture('/home/pi/Desktop/image.jpg')
camera.stop_preview()
path = "/home/pi/Desktop/image.jpg"
s = (paralleldots.facial_emotion(path))
ans = dict(s)
if ("facial_emotion" in ans):
a = list(ans['facial_emotion'])
b = dict(a[0])
emotions = b['tag']
#while True:
print(emotions)
if (emotions == "Angry"):
pygame.mixer.music.load("/home/pi/Sounds/Angry.mp3")
pygame.mixer.music.play()
else:
if (emotions == "Disgust"):
pygame.mixer.music.load("/home/pi/Sounds/Disgust.mp3")
pygame.mixer.music.play()
else:
if (emotions == "Fear"):
pygame.mixer.music.load("/home/pi/Sounds/Fear.mp3")
pygame.mixer.music.play()
else:
if (emotions == "Happy"):
pygame.mixer.music.load("/home/pi/Sounds/Happy.mp3")
pygame.mixer.music.play()
else:
if (emotions == "Neutral"):
pygame.mixer.music.load(
"/home/pi/Sounds/Normal.mp3")
pygame.mixer.music.play()
else:
if (emotions == "Normal"):
pygame.mixer.music.load(
"/home/pi/Sounds/Normal.mp3")
pygame.mixer.music.play()
else:
if (emotions == "Sad"):
pygame.mixer.music.load(
"/home/pi/Sounds/Sad.mp3")
pygame.mixer.music.play()
else:
if (emotions == "Surprise"):
pygame.mixer.music.load(
"/home/pi/Sounds/Surprise.mp3")
pygame.mixer.music.play()
if (emotions == "Neutral"):
CODE("Normal")
CODE(emotions) # morse code for the emotions
else:
print("Face is not detected clearly :( ")
GPIO.output(buzzer, GPIO.HIGH)
sleep(1)
GPIO.output(buzzer, GPIO.LOW)
# Play NoFace
pygame.mixer.music.load("/home/pi/Sounds/NoFace.mp3")
pygame.mixer.music.play()
CODE("No Face")
def analyze_text(text):
paralleldots.set_api_key(paralleldots_TOKEN)
paralleldots.get_api_key()
emotions = paralleldots.emotion(text)["emotion"]
pos = (emotions["Happy"] + emotions["Excited"])
return pos
from paralleldots import get_api_key, sentiment
get_api_key('rijmLUn8FESsEHkWLQLvolUI1mjc7tDmaj8CucrApKg')
response = sentiment("GOOD")
print response
import paralleldots
import unicodedata
import bs4
from bs4 import BeautifulSoup as soup
from six.moves import urllib
import json
import sys
print("test")
# Setting your API key
paralleldots.set_api_key("NlSzhn0HmhTaBrK9ufzeKMoyMbJI4uGBYpJkSLXz1uo")
# Viewing your API key
print("Our API key: " + paralleldots.get_api_key())
# Test getting emotion output from a string
def getEmotionsDicFromText(input):
text = input
emotionsDic = paralleldots.emotion(text)
emotionsUnicode = emotionsDic[u'emotion'][u'probabilities']
emotionsStringDic = {}
for emotion in emotionsUnicode:
emotionsStringDic[unicodedata.normalize('NFKD', emotion).encode(
'ascii', 'ignore')] = emotionsUnicode[emotion]
return emotionsStringDic