'''

In this project, you will visualize the feelings and language used in a set of

Tweets. This starter code loads the appropriate libraries and the Twitter data you'll

need!

'''

import json

from textblob import TextBlob

import matplotlib.pyplot as plt

from wordcloud import WordCloud

#Get the JSON data

tweetFile = open("tweets_small.json", "r")

tweetData = json.load(tweetFile)

tweetFile.close()

# Continue your program below!

filtered_words = ["of", "in", "the", "for", "they", "if", "with", "is", "https"]

polarity = []

subjectivity = []

i = 0

bigtweet = ""

for tweet in tweetData:

blob = TextBlob(tweet["text"])

tweet_p = blob.polarity

tweet_s = blob.subjectivity

polarity.append(tweet_p)

subjectivity.append(tweet_s)

i += 1

#bigtweet += text

#blog = TextBlob(tweet)

filtered = {}

#wordslist = bigblob.words

for word in filtered_words:

#filters here

if len(word) < 2:

continue

if not word.isalpha():

continue

if word.lower() in filtered_words:

continue

filtered[word.lower()] = blob.word_counts[word.lower()]

print(word)

final1 = sum(polarity)

len1 = len(polarity)

avg_p = final1 / len1

final2 = sum(subjectivity)

len2 = len(subjectivity)

avg_s = final2 / len2

print(avg_p)

print(avg_s)

# Textblob sample:

#tb = TextBlob("You are a brilliant computer scientist.")

#print(tb.polarity)

plt.title('Pol Histogram')

plt.xlabel('Polarity')

plt.ylabel('Rate')

plt.grid(True)

plt.hist(polarity , bins = 5)

plt.axis([-1.00, 1.00, 0, 60])

plt.show()

plt.title('Sub Histogram')

plt.xlabel('Subjectivity')

plt.ylabel('Rate')

plt.grid(True)

plt.hist(subjectivity, bins = 5)

plt.axis([-1.00, 1.00, 0, 60])

plt.show()

plt.scatter(polarity, subjectivity)

plt.show()

all_tweets = ', '.join(tweet['text'] for tweet in tweetData)

tb = TextBlob(all_tweets)

#filtered_words = []

print(all_tweets)

print(str(tweet))

wordcloud = WordCloud().generate(all_tweets)

plt.imshow(wordcloud)

plt.axis("off")

plt.show()

#generate_from_text(text)