if len(listOfWords) > 3:
numTweets[tweetLocationKey] += 1
sentiTweets[tweetLocationKey] += total / len(listOfWords)
for state in numTweets:
if numTweets[state] != 0:
sentiTweets[state] = sentiTweets[state] / numTweets[state]
if sentiTweets[state] == 0:
sentiTweets[state] = None
nationalAverage = totalNational / numWords
for stateCode in sentiTweets:
color = get_sentiment_color(sentiTweets[stateCode])
usa.setFillColor(stateCode, color)
maxSenti = max(sentiTweets.itervalues())
minSenti = min(x for x in sentiTweets.itervalues() if x != None)
for state in sentiTweets:
if sentiTweets[state] == maxSenti:
maxState = state
if sentiTweets[state] == minSenti:
minState = state
print 'National sentiment value {}'.format(('%.2f' % nationalAverage))
print 'Highest sentiment value of {} in {}'.format(('%.2f' % maxSenti),
maxState)
print 'Lowest sentiment values of {} in {}'.format(('%.2f' % minSenti),
class SentimentAnalysis:
def __init__(self):
self.sentiments = load_sentiments()
self.states = load_states()
self.tweets = load_tweets()
self.usa = None # variables should always be defined in __init__
def show_country(self): # function names should not have capital letters
self.usa = Country(self.states, 1200)
def update_sentiments(self, query_list):
state_totals = dict()
for tweet in self.tweets:
if all(query_word.lower() in tweet.message().lower() for query_word
in query_list): # if every query word is in the tweet
sent = self.weigh_message(tweet.message().lower())
place = self.place_tweet(tweet.position())
if sent != -1: # word was actually weighted
if place not in state_totals:
state_totals[place] = [0, 0]
state_totals[place][0] += sent # total sentiment value
state_totals[place][
1] += 1 # total number of tweets from state
for state in self.states:
if state.abbrev() in state_totals:
average_sent = state_totals[state.abbrev()][0] / state_totals[
state.abbrev()][1]
self.usa.setFillColor(state.abbrev(),
get_sentiment_color(average_sent))
print(state.abbrev(), state_totals[state.abbrev()][1])
else:
self.usa.setFillColor(state.abbrev(),
get_sentiment_color(None))
def place_tweet(self, position):
min_dist = 999999999999 # a really big number
close_state = None
geo_pos = GeoPosition(position[1], position[0])
for state in self.states:
dist = state.centroid().distance(geo_pos)
if dist < min_dist:
min_dist = dist
close_state = state
return close_state.abbrev(
) # using abbrev as keys instead of reference makes debugging a bit easier
def weigh_message(self, message):
total = 0
split_str = message.split()
num_words = 0
for word in split_str:
if word in self.sentiments:
num_words += 1
total += self.sentiments[word]
if not num_words:
return -1
total /= num_words
return total
