counter.py

from __future__ import unicode_literals
import tweepy #The Twitter API
from time import sleep
from datetime import datetime
from textblob import TextBlob #For Sentiment Analysis
import matplotlib.pyplot as plt #For Graphing the Data
import psycopg2
import sys
from twython import Twython, TwythonStreamer
import json
import pandas as pd
from geopy.geocoders import Nominatim
import gmplot
import csv
from collections import Counter
import ast


consumer_key = 'WRxcgZHq8HOA9AiACeoz7pc61'
consumer_secret = 'IJnOARqL3baljF5VfMPB4Gy1GmxVLlSv6L4BgJoh3bVDslSQYL'
access_token = '32554005-yIgL0lbl0aWXyJ0E8q61zDF8BpOtVzWwRoZyCDm1n'
access_token_secret = 'PzknR8jcAmNgG35G0D99BH9qEJfF7n477AxK1kgFDnVWl'

# Load credentials from json file
with open("twitter_credentials.json", "r") as file:
    creds = json.load(file)

# Instantiate an object
python_tweets = Twython(consumer_key, consumer_secret)

def process_tweet(tweet):
    d = {}
    d['hashtags'] = [hashtag['text'] for hashtag in tweet['entities']['hashtags']]
    d['text'] = tweet['text']
    d['user'] = tweet['user']['screen_name']
    d['user_loc'] = tweet['user']['location']
    return d

tweets = pd.read_csv("saved_tweets.csv")
tweets.head()


# Extract hashtags and put them in a list
list_hashtag_strings = [entry for entry in tweets.hashtags]
list_hashtag_lists = ast.literal_eval(','.join(list_hashtag_strings))
hashtag_list = [ht.lower() for list_ in list_hashtag_lists for ht in list_]

# Count most common hashtags
counter_hashtags = Counter(hashtag_list)
counter_hashtags.most_common(20)
print(counter_hashtags)


with open('hashtag.csv', "r") as file:
    writer = csv.writer(file)
    writer.writerow(counter_hashtags)