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def connect(username, created_at, tweet, retweet_count, place , location):
	"""
	connect to MySQL database and insert twitter data
	"""
	try:
		con = mysql.connector.connect(host = 'localhost',
		database='twitterdb', user='root', password = password, charset = 'utf8')
		

		if con.is_connected():

DFoly

class Streamlistener(tweepy.StreamListener):
	

	def on_connect(self):
		print("You are connected to the Twitter API")


	def on_error(self):
		if status_code != 200:
			print("error found")

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if __name__== '__main__':

	auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
	auth.set_access_token(access_token, access_token_secret)
	api =tweepy.API(auth, wait_on_rate_limit=True)

	# create instance of Streamlistener
	listener = Streamlistener(api = api)
	stream = tweepy.Stream(auth, listener = listener)

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if __name__ == '__main__':

	t = TweetObject( host = 'localhost', database = 'twitterdb', user = 'root')

	data  = t.MySQLConnect("SELECT created_at, tweet FROM `TwitterDB`.`Golf`;")
	data = t.clean_tweets(data)
	data['Sentiment'] = np.array([t.sentiment(x) for x in data['clean_tweets']])
	t.word_cloud(data)
	t.save_to_csv(data)
	

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def sentiment(self, tweet):
		"""
		This function calculates sentiment
		from our base on our cleaned tweets.
		Uses textblob to calculate polarity.
		Parameters:
		----------------
		arg1: takes in a tweet (row of dataframe)	
		----------------
		Returns: 

DFoly

#!usr/bin/python

import mysql.connector
from mysql.connector import Error
import tweepy
import json
from dateutil import parser
import time
import os
import subprocess

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def clean_tweets(self, df):
	
		"""
		Takes raw tweets and cleans them
		so we can carry out analysis
		remove stopwords, punctuation,
		lower case, html, emoticons.
		This will be done using Regex
		? means option so colou?r matches
		both color and colour.

DFoly

#!usr/bin/python

import mysql.connector
from mysql.connector import Error
import tweepy
import json
from dateutil import parser
import time
import os
import subprocess

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from sklearn.feature_extraction.text import TfidfVectorizer
data = df['body_new']


tf_idf_vectorizor = TfidfVectorizer(stop_words = 'english',#tokenizer = tokenize_and_stem,
                             max_features = 20000)
tf_idf = tf_idf_vectorizor.fit_transform(data)
tf_idf_norm = normalize(tf_idf)
tf_idf_array = tf_idf_norm.toarray()

DFoly

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.cluster import KMeans 
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import PCA
from sklearn.preprocessing import normalize
from sklearn.metrics import pairwise_distances

import nltk
import string