def word_count():
file1 = open("gram.txt", "r")
a = file1.read()
b = TextBlob(a)
words = b.word_counts()
print(words)
tweets = tweets + tweet_data[e]['text'] #convine all your string or tweets, will be used later in WordCloud function
textbird_tb = TextBlob(tweets)
undesired_words = ["hi", "bye", "interesting", "goodnight", "spider", "fear"]
filtered_dictionary = {}
filtered_words[words] = count
for word in textbird_tb.words:
if(len(word) < 2):
continue
elif( not word.isalpha()):
continue
elif(word in undesired_words): #the continue means that if the condition is not met, the elif condition will be broken and it will go back to the beginning of the for loop.
continue
filtered_dictionary['word'] = textbird_tb.word_counts(word)
#Set up you HISTOGRAM
plt.his(polarity, bins = [-1.1, -.75, -.5, -.25, 0, .25, .5, .75, 1.1])
plt.xlabel('Polarity')
plt.ylabel('Frequency')
plt.title('Polarity vs Frequency')
plt.axis([-1.1, 1.1, 0, 100]) #describes how high the x and y axis will go. X is first and Y is after.
plt.grid(True)
plt.show()
word_Cloud = WordCloud().generate_from_frequencies(filtered_dictionary)
plt.imshow(word_Cloud, interpolation = "bilinear")
plt.axis("off")
plt.show
