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Update to Python 3 #2

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Update to Python 3 #2

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30 changes: 20 additions & 10 deletions doAnalysis.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,15 @@
"""
Author: Stephen W. Thomas
I modified the script to work with Python 3.
Hint: if you install all libraries and you get an error:
"Matplotlib Python is not installed as a framework"
use the following code in the terminal:

Perform sentiment analysis using TextBlob to do the heavy lifting.
echo "backend: TkAgg" >> ~ / .matplotlib / matplotlibrc

Requirements:
textblob
numpy
matplotlib
"""

# The main package to help us with our text analysis
Expand Down Expand Up @@ -51,9 +59,10 @@ def strip_non_ascii(string):
# clean: The preprocessed string of characters
# TextBlob: The TextBlob object, created from the 'clean' string

with open('newtwitter.csv', 'rb') as csvfile:
with open('newtwitter.small.csv', encoding = "ISO-8859-1") as csvfile:
reader = csv.reader(csvfile, delimiter=',')
reader.next()
next(reader)

for row in reader:

tweet= dict()
Expand Down Expand Up @@ -135,25 +144,26 @@ def strip_non_ascii(string):

# First, print out a few example tweets from each sentiment category.

print "\n\nTOP NEGATIVE TWEETS"
print("\n\nTOP NEGATIVE TWEETS")
negative_tweets = [d for d in tweets_sorted if d['sentiment'] == 'negative']
for tweet in negative_tweets[0:100]:
print "id=%d, polarity=%.2f, clean=%s" % (tweet['id'], tweet['polarity'], tweet['clean'])
print("id=%d, polarity=%.2f, clean=%s" % (tweet['id'], tweet['polarity'], tweet['clean']))

print "\n\nTOP POSITIVE TWEETS"
print("\n\nTOP POSITIVE TWEETS")
positive_tweets = [d for d in tweets_sorted if d['sentiment'] == 'positive']
for tweet in positive_tweets[-100:]:
print "id=%d, polarity=%.2f, clean=%s" % (tweet['id'], tweet['polarity'], tweet['clean'])
print("id=%d, polarity=%.2f, clean=%s" % (tweet['id'], tweet['polarity'], tweet['clean']))

print "\n\nTOP NEUTRAL TWEETS"
print("\n\nTOP NEUTRAL TWEETS")
neutral_tweets = [d for d in tweets_sorted if d['sentiment'] == 'neutral']
for tweet in neutral_tweets[0:500]:
print "id=%d, polarity=%.2f, clean=%s" % (tweet['id'], tweet['polarity'], tweet['clean'])
print("id=%d, polarity=%.2f, clean=%s" % (tweet['id'], tweet['polarity'], tweet['clean']))


# Next, create some plots

# A histogram of the scores.

x = [d['polarity'] for d in tweets_sorted]
num_bins = 21
n, bins, patches = plt.hist(x, num_bins, normed=1, facecolor='green', alpha=0.5)
Expand Down