# dictionary of tweets indexed on date
# will contain following structure:
# [[list of sentiment for days tweets], stock_market_close - stock_market_open]
trump_tweets = {}
# pull all tweets
api = twitter.Api(CONSUMER_KEY, CONSUMER_SECRET, ACCESS_TOKEN_KEY, ACCESS_TOKEN_SECRET)
screen_name = 'realDonaldTrump'
timeline = get_tweets(api=api, screen_name=screen_name)
# create ToneAnalyzer IBM Watson object
tone_analyzer = ToneAnalyzerV3(
version='2017-09-21',
iam_apikey='n54NtzfmzFug46PryonqHU9sGWWaGKcT_vQ_zQhOvdrR',
url='https://gateway.watsonplatform.net/tone-analyzer/api'
)
first_tweet = 0
earliest_date = ''
recent_date = ''
# add tweets and sentiment to dictionary
for tweet in timeline:
dt = parser.parse(tweet._json['created_at'])
date = datetime.datetime.strptime(str(dt.day) + str(dt.month) + str(dt.year), '%d%m%Y').date()
if first_tweet == 0:
recent_date = date
first_tweet = 1
import json,httplib,urllib
from watson_developer_cloud import ToneAnalyzerV3
import csv
import pandas
os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = 'WhizLeadsInsights.json'
consumer_key= '4F4rkhWlzJx1geKY7EIFoyOyp'
consumer_secret= '6uvr35kgy7CziY8zzGlHbywAVNEb8qzMaxs0DnL5lupH8HYH9D'
access_token='801128036364091392-3edsjInInkhwUR87PblYwKsuGmPsHob'
access_token_secret='KEL1QWcLZy1TsG4gloLB1w1wmme5Iu6b65wje5VubNjxM'
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)
tone_analyzer = ToneAnalyzerV3(
username='******',
password='******',
version='2016-05-19')
PARSE_PRODUCTION_APLICATION_ID= "9LT6MCUSdT4mnzlNkG2pS8L51wvMWvugurQJnjwB"
PARSE_PRODUCTION_REST_API_KEY = "6gwEVURQBIkh9prcc3Bgy8tRiJTFYFbJJkQsB45w"
PARSE_DEVELOPMENT_APLICATION_ID= "9LT6MCUSdT4mnzlNkG2pS8L51wvMWvugurQJnjwB"
PARSE_DEVELOPMENT_REST_API_KEY = "6gwEVURQBIkh9prcc3Bgy8tRiJTFYFbJJkQsB45w"
if (PRODUCTION_ENVIRONMENT):
{
Parse_Application_Id = PARSE_PRODUCTION_APLICATION_ID
Parse_REST_API_Key = PARSE_PRODUCTION_REST_API_KEY
}
else:
def watson():
tone_analyzer = ToneAnalyzerV3(
version='2017-09-21',
username='******',
password='******')
f = open('sent.txt', 'r')
stri = ""
for line in f:
stri = stri + line
text = stri
content_type = 'application/json'
color = [0, 0, 0, 0]
tone = tone_analyzer.tone({"text": text}, content_type, sentences=False)
obj = json.dumps(tone, indent=2)
l = len(tone['document_tone']['tones'])
for i in range(0, l):
if (tone['document_tone']['tones'][i]):
temp_obj_score = tone['document_tone']['tones'][i]['score']
temp_obj_id = tone['document_tone']['tones'][i]['tone_id']
if (temp_obj_id == 'joy'):
color[0] = temp_obj_score
elif (temp_obj_id == 'sadness'):
color[1] = temp_obj_score
elif (temp_obj_id == 'tentative'):
color[2] = temp_obj_score
elif (temp_obj_id == 'analytical'):
color[3] = temp_obj_score
output_file("graph.html")
emotions = ['Joy', 'Sadness', 'Tentative', 'Analytical']
repos = ['average', 'you']
data = {
'Emotions': emotions,
'average': [0.58125, 0.532308, 0.717059, 0.696667],
'you': color
}
source = ColumnDataSource(data=data)
p = figure(x_range=emotions,
y_range=(0, 1),
plot_width=500,
plot_height=800,
title="Sentiment Averages",
toolbar_location=None,
tools="")
p.vbar(x=dodge('Emotions', -0.15, range=p.x_range),
top='average',
width=0.3,
source=source,
color='#c9d9d3',
legend=value("Average"))
p.vbar(x=dodge('Emotions', 0.15, range=p.x_range),
top='you',
width=0.3,
source=source,
color='#718dbf',
legend="Your repo")
p.y_range.start = 0
p.x_range.range_padding = 0.1
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None
show(p)
from __future__ import print_function
from watson_developer_cloud import ToneAnalyzerV3
import pandas as pd
import json
#Instantiate TA Object with your Credentials
service = ToneAnalyzerV3(
#url is optional, and defaults to the URL below. Use the correct URL for your region.
url='https://gateway.watsonplatform.net/tone-analyzer/api',
version='2017-09-21',
iam_apikey='apikey')
#read data
data = pd.read_csv(
"/Users/pudin/Desktop/digital/project1/russian_dataset_with_datesplit_clean.csv"
)
#make a test copy
test_data = data.head(50).copy()
#get result
for index, review in data['Ad_Text '].iteritems():
result = json.dumps(service.tone(tone_input=review,
content_type="text/plain").get_result(),
indent=2)
for i in json.loads(result)['document_tone']['tones']:
print(index, i['tone_name'], i['score'])
#save tone and score to dataframe
data.at[index, str(i['tone_name'])] = float(i['score'])
data.to_csv('test_data.csv')
# -*- coding: utf-8 -*-
import json
from watson_developer_cloud import ToneAnalyzerV3
# BEGIN of python-dotenv section
from os.path import join, dirname
from dotenv import load_dotenv
import os
dotenv_path = join(dirname(__file__), '.env')
load_dotenv(dotenv_path)
# END of python-dotenv section
tone_analyzer = ToneAnalyzerV3(username=os.environ.get("TONE_USERNAME"),
password=os.environ.get("TONE_PASSWORD"),
version='2016-05-19')
def parse_toneanalyzer_response(json_data):
"""Parses the JSON response from ToneAnalyzer to return
a dictionary of emotions and their corresponding score.
Parameters
----------
json_data: {dict} a json response from ToneAnalyzer (see Notes)
Returns
-------
dict : a {dict} whose keys are emotion ids and values are their corresponding score.
"""
emotions = {}
#pass the transcript to the tone analyzer, which recognizes emotions;
#return predominat emotion
from __future__ import print_function
import json
from watson_developer_cloud import ToneAnalyzerV3
from io import StringIO
tone_analyzer = ToneAnalyzerV3(username="******",
password="******",
version="2016-05-19")
def getPredominantEmotion(transcript):
tone_raw_json = StringIO()
json.dump(
tone_analyzer.tone(tone_input=transcript, content_type="text/plain"),
tone_raw_json)
#extract the emotions out of the json
score = 0
tone_str_json = (json.loads(tone_raw_json.getvalue()))
for i in range(0, 5):
aux_score = (tone_str_json["document_tone"]["tone_categories"][0]
["tones"][i]["score"])
aux_emotion = tone_str_json["document_tone"]["tone_categories"][0][
"tones"][i]["tone_name"]
print(aux_emotion + " => " + str(aux_score))
#get maximum score emotion
if (aux_score > score):
from watson_developer_cloud import ToneAnalyzerV3
tone_analyzer = ToneAnalyzerV3(
version='2018-11-17',
iam_apikey='0BhiGpM_2XfxIs6vPS1J6Eg8FSdrHM2kyf8yiF1h35HT',
url='https://gateway.watsonplatform.net/tone-analyzer/api')
def analyze(text):
tone_analysis = tone_analyzer.tone(
{
'text': text
},
'text/plain',
).get_result()
return tone_analysis
# Author: Sahil Verma
'''Importing necessary lib csv for output, watson_developer_cloud for Analyse
matplotlib for ploting, numpy to make array '''
import csv
from watson_developer_cloud import ToneAnalyzerV3
import matplotlib.pyplot as plt
import numpy as np
# Taking input
texted = input("Enter the sentence to analyse \n")
# API request from IBM Watson
tone_analyzer = ToneAnalyzerV3(
username='', # Enter the username and password provided by bluemix
password='',
version='2016-05-19 ')
result = tone_analyzer.tone(
text=texted) # Getting output as a file of dictionary
xyz = []
for x in result["document_tone"]["tone_categories"][0].get(
"tones"): # Selecting specific Emotion is result
xyz.append(x)
print(xyz) # Printing Emotions as text
# Writing Input and Output to csv file
def write2csv():
f = open("output.csv", "w")
prompt = " [y/N] "
else:
raise ValueError("invalid default answer: '%s'" % default)
while True:
choice = input(question + prompt).lower()
#choice = raw_input().lower()
if default is not None and choice == '':
return valid[default]
elif choice in valid:
return valid[choice]
else:
print("Please respond with 'yes' or 'no' "\
"(or 'y' or 'n').\n")
tone_analyzer = ToneAnalyzerV3(username='', password='', version='2017-09-26')
while True:
utterance = input(
"Enter the sentence to analyze for tone (can put more than one): ")
print("\nanalyzing....\n")
print(
json.dumps(tone_analyzer.tone(tone_input=utterance,
content_type="text/plain"),
indent=2))
again = query_yes_no("Want to try another?")
if not again:
break
#!usr/bin/env python
from __future__ import print_function
import json
import os
from os.path import join, dirname
from watson_developer_cloud import ToneAnalyzerV3
tone_analyzer = ToneAnalyzerV3(
username='******',
password='******',
version='2017-09-26')
#KodK0d122017.
#print("tone_chat() example 1:\n")
#utterances = [{'text': '"More that all @HerdFB away games and half' + 'their of their \n '
#'home games. Oh its True ... '
#'Damn True! #IamBecauseWeAre https://t.co/GZ1qHgixNK"', 'user': '******'},{'text': 'It is a good day.', 'user': '******'},{'text': 'I am very happy. It is a good day.', 'user': '******'}]
#print(json.dumps(tone_analyzer.tone_chat(utterances), indent=2))
directory = './resources/'
OutFile = ''
print("hi")
print("Analyzing all files in " + directory)
for fn in os.listdir(directory):
print("File " + fn + " " +fn[len(fn)-4:len(fn)])
if fn[len(fn)-4:len(fn)] == 'json':
OutFile = directory +fn[0:len(fn)-4]+'_ans.json'
print("working on file"+fn)
#with open(join(dirname(__file__),'./resources/tone.json')) as tone_json:
# tone = tone_analyzer.tone(json.load(tone_json)['text'], "text/plain")
from django.shortcuts import render, get_object_or_404
from .forms import PostForm
from django.shortcuts import redirect
import json
import requests
from watson_developer_cloud import ToneAnalyzerV3
from watson_developer_cloud import LanguageTranslatorV3
language_translator = LanguageTranslatorV3(
version='2018-05-01',
iam_apikey='h3H1OGBNlGD3QK37rcuGUMCaJpmQHWAewE1ZpzTgd_Zm')
service = ToneAnalyzerV3(
version='2017-09-21',
iam_apikey='Ruy6zs3ZAw5TmvGXMxVJQ-QUP86LucgVWHkHC1cPrkdb',
)
def post_list(request):
posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('published_date')
for post in posts:
posting = post.text
translation = language_translator.translate(text=posting, model_id='en-es').get_result()
obj = (json.dumps(translation, indent=2, ensure_ascii=False))
obj2 = json.loads(obj)
post.obj2 = obj2['translations'][0]['translation']
post.w_count = obj2['word_count']
from __future__ import print_function
import json
import firebase_admin
from firebase_admin import credentials
from firebase_admin import db
import threading, time
from pprint import pprint
from os.path import join, dirname
from watson_developer_cloud import ToneAnalyzerV3
from watson_developer_cloud.tone_analyzer_v3 import ToneInput
#If service instance provides API key authentication
service = ToneAnalyzerV3(
version='2018-11-01',
url='https://gateway-syd.watsonplatform.net/tone-analyzer/api',
iam_apikey='J4a1j1p3o6zKwoSPqJwYQ_l40lNRU85DaTLrQFVKGfbq')
#################################3
cred = credentials.Certificate(
'C:/Users/Nirvan Dogra/PycharmProjects/TwitterAPI/simulation.json')
firebase_admin.initialize_app(
cred, {'databaseURL': 'https://simulation-1270b.firebaseio.com/'})
while (1):
result = db.reference('messages/msg').get()
print(result)
if result == "empty":
print("null" + result)
continue
from watson_developer_cloud import ToneAnalyzerV3 tone_analyzer = ToneAnalyzerV3( username='******', password='******', version='2016-05-19')
from flask import Flask
from flask_restful import Api, Resource, reqparse
import json
from watson_developer_cloud import ToneAnalyzerV3
import APIkey
app = Flask(__name__)
api = Api(app)
tone_analyzer = ToneAnalyzerV3(
version= APIkey.version,
iam_apikey=APIkey.iam_apikey,
url=APIkey.url
)
def anger_score(data):
doc_tone = data.get('document_tone')
tones = doc_tone.get('tones')
threshold = 0
for tone in tones:
score = tone.get("score")
tone_id = tone.get("tone_id")
if score > threshold and tone_id == "anger":
return score
class Tone(Resource):
def get(self, input):
text = input
tone_analysis = tone_analyzer.tone({'text': text},'application/json').get_result()
data = json.loads(json.dumps(tone_analysis, indent=2))
